{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b299ee9",
   "metadata": {},
   "source": [
    "# Quick start skforecast\n",
    "\n",
    "Welcome to a quick start guide to using **skforecast**! In this guide, we will provide you with a code example that demonstrates how to create, validate, and optimize a recursive multi-step forecaster, `ForecasterRecursive`, using skforecast.\n",
    "\n",
    "A **Forecaster** object in the skforecast library is a comprehensive container that provides essential functionality and methods for training a forecasting model and generating predictions for future points in time.\n",
    "\n",
    "If you need more detailed documentation or guidance, you can visit the [User Guides](../user_guides/table-of-contents.html) section.\n",
    "\n",
    "Without further ado, let's jump into the code example!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1ae70e1-1354-4aa6-9dc4-73d3c7b016c5",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a84efd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from skforecast.datasets import load_demo_dataset\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.model_selection import grid_search_forecaster\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb7062ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "1991-07-01    0.429795\n",
       "1991-08-01    0.400906\n",
       "1991-09-01    0.432159\n",
       "1991-10-01    0.492543\n",
       "1991-11-01    0.502369\n",
       "Freq: MS, Name: y, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = load_demo_dataset()\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52e8871d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates : 1991-07-01 00:00:00 --- 2005-06-01 00:00:00  (n=168)\n",
      "Test dates  : 2005-07-01 00:00:00 --- 2008-06-01 00:00:00  (n=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data partition train-test\n",
    "# ==============================================================================\n",
    "end_train = '2005-06-01 23:59:00'\n",
    "print(\n",
    "    f\"Train dates : {data.index.min()} --- {data.loc[:end_train].index.max()}  \" \n",
    "    f\"(n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Test dates  : {data.loc[end_train:].index.min()} --- {data.index.max()}  \"\n",
    "    f\"(n={len(data.loc[end_train:])})\"\n",
    ")\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[:end_train].plot(ax=ax, label='train')\n",
    "data.loc[end_train:].plot(ax=ax, label='test')\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9801a209-b601-4d9b-aa3c-a2e03df98c98",
   "metadata": {},
   "source": [
    "## Train a forecaster\n",
    "\n",
    "Let's start by training a forecaster! For a more in-depth guide to using <code>ForecasterRecursive</code>, visit the [User guide](../user_guides/autoregressive-forecaster.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2a1c509",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            font-weight: bold;\n",
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       "        .container-14270aa44fc64e0087ac958c42c8dcc4 li::before {\n",
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       "            color: #001633;\n",
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       "    </style>\n",
       "    \n",
       "        <div class=\"container-14270aa44fc64e0087ac958c42c8dcc4\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_10']</li>\n",
       "                    <li><strong>Window size:</strong> 15</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 14:31:13</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 14:31:15</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] \n",
       "Window features: ['roll_mean_10'] \n",
       "Window size: 15 \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 14:31:13 \n",
       "Last fit date: 2025-11-26 14:31:15 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit a recursive multi-step forecaster (ForecasterRecursive)\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=10)\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data.loc[:end_train])\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886bd183",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To understand what can be done when initializing a forecaster with <b>skforecast</b> visit <a href=\"../quick-start/forecaster-parameters.html\">Forecaster parameters</a> and <a href=\"../quick-start/forecaster-attributes.html\">Forecaster attributes</a>. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "733cef42-c7bf-42f8-8c3d-8471638599a4",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "After training the forecaster, the `predict` method can be used to make predictions for the future $n$ steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f801e20d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    1.026507\n",
       "2005-08-01    1.042429\n",
       "2005-09-01    1.116730\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=len(data.loc[end_train:]))\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "617c170f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[:end_train].plot(ax=ax, label='train')\n",
    "data.loc[end_train:].plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c259c00f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (mse): 0.006632513357651682\n"
     ]
    }
   ],
   "source": [
    "# Prediction error on test data\n",
    "# ==============================================================================\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = data.loc[end_train:],\n",
    "                y_pred = predictions\n",
    "            )\n",
    "print(f\"Test error (mse): {error_mse}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dbe3c4dc-4b71-42f0-a4c9-d6815535272d",
   "metadata": {},
   "source": [
    "## Backtesting: forecaster validation\n",
    "\n",
    "In time series forecasting, backtesting refers to the process of validating a predictive model using historical data. The technique involves moving backwards in time, step-by-step, to assess how well a model would have performed if it had been used to make predictions during that time period. Backtesting is a form of cross-validation that is applied to previous periods in the time series.\n",
    "\n",
    "Backtesting can be done using a variety of techniques, such as simple train-test splits or more sophisticated methods like rolling windows or expanding windows. The choice of method depends on the specific needs of the analysis and the characteristics of the time series data. For more detailed documentation on backtesting, visit: [User guide Backtesting forecaster](../user_guides/backtesting.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd3fded2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 168\n",
      "Number of observations used for backtesting: 36\n",
      "    Number of folds: 4\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "    Last fold only includes 6 observations.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-06-01 00:00:00  (n=168)\n",
      "    Validation: 2005-07-01 00:00:00 -- 2006-04-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Training:   1991-07-01 00:00:00 -- 2006-04-01 00:00:00  (n=178)\n",
      "    Validation: 2006-05-01 00:00:00 -- 2007-02-01 00:00:00  (n=10)\n",
      "Fold: 2\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-02-01 00:00:00  (n=188)\n",
      "    Validation: 2007-03-01 00:00:00 -- 2007-12-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-12-01 00:00:00  (n=198)\n",
      "    Validation: 2008-01-01 00:00:00 -- 2008-06-01 00:00:00  (n=6)\n",
      "\n"
     ]
    },
    {
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       "model_id": "f32bf3a8c3dd4783b4eb333cbf213a09",
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>2005-07-01</th>\n",
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       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.116730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.162179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.143455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.160991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1.126347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.685447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.667809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.691714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.691262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.818445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.902810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.993965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1.060320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1.101023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1.146837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1.159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1.158871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.604494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.706613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.659741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.852929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.802784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.919339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1.021892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1.012373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1.124584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-11-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1.127115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-01</th>\n",
       "      <td>2</td>\n",
       "      <td>1.173850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>1.161372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.598217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.698431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.571306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.735845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-06-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.795137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2005-07-01     0  1.026507\n",
       "2005-08-01     0  1.042429\n",
       "2005-09-01     0  1.116730\n",
       "2005-10-01     0  1.162179\n",
       "2005-11-01     0  1.143455\n",
       "2005-12-01     0  1.160991\n",
       "2006-01-01     0  1.126347\n",
       "2006-02-01     0  0.685447\n",
       "2006-03-01     0  0.667809\n",
       "2006-04-01     0  0.691714\n",
       "2006-05-01     1  0.691262\n",
       "2006-06-01     1  0.818445\n",
       "2006-07-01     1  0.902810\n",
       "2006-08-01     1  0.993965\n",
       "2006-09-01     1  1.060320\n",
       "2006-10-01     1  1.101023\n",
       "2006-11-01     1  1.146837\n",
       "2006-12-01     1  1.159200\n",
       "2007-01-01     1  1.158871\n",
       "2007-02-01     1  0.604494\n",
       "2007-03-01     2  0.706613\n",
       "2007-04-01     2  0.659741\n",
       "2007-05-01     2  0.852929\n",
       "2007-06-01     2  0.802784\n",
       "2007-07-01     2  0.919339\n",
       "2007-08-01     2  1.021892\n",
       "2007-09-01     2  1.012373\n",
       "2007-10-01     2  1.124584\n",
       "2007-11-01     2  1.127115\n",
       "2007-12-01     2  1.173850\n",
       "2008-01-01     3  1.161372\n",
       "2008-02-01     3  0.598217\n",
       "2008-03-01     3  0.698431\n",
       "2008-04-01     3  0.571306\n",
       "2008-05-01     3  0.735845\n",
       "2008-06-01     3  0.795137"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 10,\n",
    "         initial_train_size = len(data.loc[:end_train]),\n",
    "         refit              = True,\n",
    "         fixed_train_size   = False\n",
    "     )\n",
    "\n",
    "metric, predictions_backtest = backtesting_forecaster(\n",
    "                                   forecaster    = forecaster,\n",
    "                                   y             = data,\n",
    "                                   cv            = cv,\n",
    "                                   metric        = 'mean_squared_error',\n",
    "                                   verbose       = True,\n",
    "                               )\n",
    "\n",
    "display(metric)\n",
    "predictions_backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec0d4cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YxYPeUW74/Sr++oz+swO2UuB3OeQ8msTjNmUqnj/Pg9FDFCytU3HPg73IqVOxerUh0FzOicc8l6qJWWIoFcsA+GSeXtTjM3YXjfy203l0dLa1XRTEUNe0zbwuFT4bxst1BlPbI1f+4z6MHzMCC5eswj/ufFxbj98fwJtP/AtPvfwurr75AeTl5uDyc0/CPf+4GL8//a+oHlKOu268CNfd9gje+uBLFBXkY7utpmozju9+9GVsOm4UigoLcN5VemukllbjxMMwDJPNDB2D4PYHoGlDM9zff4hBxdAxpikyRbrbGMp5FOKxQXuer6TS/nzH/kLWJudRg0LpftP3EtlligKXS8Gvq1Xc/34QB01RsPU0XRhd93IQj3wcxJl7uTCyUkHvGBdQF3BUPI4Y70LhEAVrm1UceYsfxxuG8eouYJwh1uwu4BHMLCRXEVirAJNH6RvhswXGCFuT85hVTcKzkbbt4s1utGkg+Jep2fftGzrR6/Oju7sHDY0tmnj886m/x9z5S3HT7Y+Hnnf+Vbfh+3cfwSajR6CwIF9rrv7m+19gzVo9D2b+4hWh53b19CInx6u9HsMwzIChuFx7UD1Gb8PBxNDR4a+pnyMJxlDOY4f+2Cycx0oH8h37CVlHi0eXfZJhtymRDtp7b/iw3XAv6upVPPBBENT3+uVvgvjTfm7kT3JePPaO0Nf3+uwg6lqB0UZWwSo9y0CrqnHKedzRCFnPL3dhM5eCBbUq1hlSQDiPNNw6j8Xjxsdmk8Zhx20210LW0YwZNRwff/kDPv1qDj54/g589OVs7fs33vscre3GCYZhGGYgQu1hHKrezVrnUYjHdcsjcx4jwtY2ice8QmCYsY5lyTiPpkpsG/MedzOKZT76VRdGpT6g8vVevFSnQCVbD8CLhngsGO1CMJeqrVXb52kTnUGgZ4T+t39srG+MMZpwhRCPZCg7dEjvZDRJ7xmuv+Gn88NGWUg80q62UeFtFOKx5GtX7LC12w1fICA9bC2Dwvx8vPfxt7j+tkf6/Le6hiYEg0EcdeYV2GbGFOy2w5Y4+egDccn/HY8DjrsQq2r1XmAMwzADVjw61DcwqybJVI4Ify/G/ZmrrU1NuX3FQ2LMNJFA5XB9JCGNQuxoTeIXVD3vkcS+Tfts4nBgeJmCrl4V3y7Wr9jDvfp/W6vXo2gsWgv8tELF9DEKuse64f0VtlJu/LnfuBRMKVIQ9Kv4apG+PtHjcXlneC/lGWLTThSo2LFY2ysYZuQ7fmoUy0SErenQYvGYeg6iJh4VBb4gberM4/P54XKFRe7P85fggD131IRgIBA/vP7tnHnav1vufQbfvPUg9ttje9z3xH/h8/ngNr0ewzDMQBKPcFHr50FE1Sjtbw4hxv2RE2gumNnQrD0E8gvtuUCL7b8hhZSngCEebXKLd99M3y4kzLr1nuQYYUxvqTWJR+LFr4OYPsaN7k2or2KC/pQSqDQE7OoqF6aQrqgPagKXGGNkXSzphCNOn+CqA1xQZ7pRX+nC8BwFvoCKLxeqsZ1HrdWQPbF0Vh8OQSJxxrRNtSprqqh+5Nk3UFZahLtuughbTN0UY0YO0xzGf//tz5rI3HLaRPzplN9h+mYTUDOsCvvvuQMqy0uxaNlq4/XqMWXTsRg/pkZ7PY9nEIaAGIYZeAjxQu4X/RuM+Y6JnMdQaxyblEih0dGjoy353wkadpZNa9p1M13gfGKEhIkRYtx3b6T4+XyBbrb4SxTHch6LRuvX1+K1QW3iTJkbKDH+2/Ju58TjlBrgtIM88FG4Okd3au/7XxAdPeHnBMkopv+h9dgoCzYK53EgcO+jL+O2687DRy/ehfz8XGy7/yk45KSLcfmfT8LTd1+DXK8Xq9fW48MvZmsh6/aOTmy/1VScNutgrap6zdp6XPOvB/Hh599rr/fkS+9o7X/eeuoW7b8fceql+PK7JJKfGYZhMkmRIR5FDp0x/m7Q5DuuW6HnHPYRj0Y+u9gedhWnFAjxmEzIOjLv0Y481TwvsP2mIt8x2Ec8rolyHoUzaXdrHBo1WOwGqKXk9An6G+XUBrFtkYJ9ytTQ2rqCejjb5VGQa7OiGjVEX4e7NYhnnvHjgh8VxApcalkGLn3b2gWLR4dYurIWh//hkj45mKdecGPM5y9ethqz/nh13Ndram7DMWddacNKGYZhHHAeba7ezVrxuOj7sHjMyQu7edHOo8drT1hfbP9OK86jfPE4bZQSmpCycK3xNlAx1BA+tVH3Fj3G99SU287xhMJ17BmioDBPQU+nCk+Tirs3UTHECwRU4OLl+gLUoL0FMzMKVG2G9bZD9ffztKh4fSliCsdQa06vvU7oIIoZMAzDMI5BTahPuxHY7+T44nGwFM1Qc23KeSQWzg73eqwYpn/t6wX8hsUWMKklO3IMC607j3aI/UlGDRH1dxQMzdE6zcCvAg3R4tHvTGscMV2mzahoXrA4qGUPknD0BYFZixQ82+iK6KNuh/O4Wb6Kr6YH8fqUIM7bVP+Z0qHiq/b4vyNEpZ1haxaPDMMwjHxGjAeG1ABTd4wUlCSaBA6MvEvoBBo9J22nfBiQkwv09gANq/TJLsSwsZHFMoS5Cbcd4lqI91TEo1BHNuyviSN0Bbiw1pTvaKq0VqMKPnpNYtJOZ02IR5/R3/HtuUZ7nCDwu4UuvGAIRyIonEcb1nNKtao5rCSi2/P0bTG7HtiQYFiJ0Po5NlqzLB4ZhmEY+VQM1R+9OWHBYnYdiUz1eqQeiideBZxwJeBEs3LRV5GEI3W7pjY5ZvEoQtbRzqOt4jH5sLUiGoXbsJ5Jw3WBs2BtWDwODxXL9H1+aHYziTWvvW16gjmAu1pf37M/qThkngs7/uzC681KTG2d45afdzmrSt8uJy924RtjpvftixP/nhCPduZgsnhkGIZh5FMxPPx1WVVs8ZipsHX1KP29SURuuYcD72dUWtcZU8L6iEfT8AcSl0KNUG/ILCqYsUPsC+fR1xgWjzVGm57oYpmIsLXNfRUrvSp6alzaSOD5a/QJLm+0KPjR1Nexj3iU7PQdWK6HyWk7vNsCjCjXX3+tYVzHwy/Eo9u+7cPikWEYhpFPueE8msWjudI6k1NmxHqIHQ6ESu6oE85jSDzqjcBDeZBm59EcupYtrt1URVGQNa16ygqAoaW6ILqpNNCn0rrWF1uMBSgZUhNrsLVgpsdo0fPuT4lHHQdC4lGuWPtDtf6+TzQoCCoKhpXpP69tVpMSj3ZuHxaPDMMwjHxEMYi5IXa2OI9iPURhCYJb7mnv+1WPie08CmcxWjyK0LXs7SOKZagdUI+pu3V/iLC15IIZ4Tq6NqjYxA1UGeIrUdhaW45w1mzU/FWUomrkO777Y2KxFqDSa22qnbz3H+5VsY8hFh+pV1BZRGF6RevhWNdPf3cWjwzDMMzAg5p/m929kPNoXA0FGXMeq/XH1Yu0h8C2+yFIxTx2QH8ziTaKbTasjhSPgm5T2NrcV5GcwgznO5rjsqrkghmR7+hp0R22LQoTT5eJ1rK5NhaETBinQM1R0NGuYs6KfsRjUKxH3vsfX6VSQTk+awMWdSsYbtR21bcB/sRGaGhEoZdewCZYPDIMwzByKRsS6Zplm/NI6yO+eBVoWgcUFKNt6i72vJdoBt7WFG4AHi0enXYeO1PId7Qx53Gi0aaH+hYSWxRGz7WOE7Z2wFkbO1GXR/MXBLU01EQEQmJN1rurOKlaDbmORCjf0dhWiWDnkWEYhhm4IWvRwyTbCmaEmG2uAz5/RfuyffJ29rxXaIKMqTFfv+LRUCMeT5Y4j/bkPEY7j9ONdEwaAZiM82inOBoyQVeCs01Tb/oTa15JTuiUfGBiPtAZAF5o1F9zuCEea5v6//1eI4xu59RiFo8bIV+/+QBOnXVw6PvaOa9h399sn9ZryniNdPj9wXti3qdP2/oeO8ycpv2dJcVG7IRhGOt9DYm1S/XHkgo9lB1VMJORsHV+sd5rksaCtDYCa5drPw7m5Nv0flGzq4X6aW+KKx7DrXFkh60tVFqbrT7JYWuR8yicx+mFKnIVFZXGnx2r2jpSrMEWNh8NeIsUKD4VXy9Qk948XkmbZ7SRQbGwO9zPcYQRtl7bT7FMZNiaq62ZNNhiz+PxwWffJfXcC848Bu89e1tar7GxMnJEtSYup04aJ/V1My3MGcY253HlfH1yColEEpDC+QqJowyIR+GCtrfo4WExDtAuIZsXQzxGu4+Oha0tNAg3i1mJBTPlhUBViSEeW9WQ4zYuT//vXUGgJVyAHYHPZ6/zuPd0V2iWdV13/8/3i85KHnnFMtEFQyHnsZ9iGcIXErOc8zjo8EoMVzQ0tqBX3Ipk8DUYhhlkDcIb1+runihSEeJlQ0vmZlsL8Sja5QjxaFcIPZbzGC0eowtm/DY5jwVirnWC2XYOTZiZZLiO7vYgWruBRp8+q3rPUnOxTGzx4xfVzTbtsn220KVR7soAGpO47PmFWJOkqEKtikw5nyHnsal/N7HHpqwHM4NksGhmeeGBG7Bg8Qq4FAWH7b87/P4AHn3+TfzzricjQs1Pv/wexo0eoblQb37wJc678lZsO2MzXHrOCZi+2QQ0t7ThrQ++wg3/eRRd3T3a71WWl+KWq8/BztttoQm8v9/5eExn6+TzrsfbH36lfT+8uhJXnPcH7L7jVsjJ8WLR0lW47MZ7sOkmo3DBmceGfoc498pb8dyr7/d5jckTxuCai0/D1tMna2t58/0vcPXND6KzS79N+/c156K0uBDf/PArzjj+UHi9Hrz6zqe48p/3a38/ceLv98dpxx2CEUOHoH1DB779YR5Ou/DGhNuSts1fz/uD9jtffT8XF/7tdtTW6SfhMSOH4eoLTsFW0yehID8Pi5atxo3/eRSffv1j6PdzvB5cdPYsHLbfbqisKEPtugbc8dALePqV9/q8V15eDh76+19QXJSP4/90Db5580Ht5+89+x/t8YvvfsaRp16mfX3sYXtrf+eomqFYXVuPB59+DY8+92boRuDqC0/B/nvuiNKSIqxvbMFjL7ylvS/td+Khf1+uPa6qrcN2+5+a1HHFMFkftqacQhJplcP1htiGQFPam6CWDpFevZuSeGxp6OPyqU6Kx5ZEzqMhaKXnPFoNW8t3HkPisUXFIgrPBoDflAL7lvV13aIR2toO8UgibeooBQiqyF0TRJPflYLzqEpZQ6hVkWnY0PCy5J3HXvumSW5c4jE/Rq8nxcg/8ASobkkuXQkO6nj87qA98Nx/38eBx12AzadOwD+v+D+sWdeAp156N/ScM084DP++7xnccu/TITH05F1X4+93PoHzr75NE4rXX3Imbrj0TJx3lR5avvXaczGsqgK/O+1y+Px+XPeX0zGkPKodhgkSVS8+eCPW1Tfi1PNvwNr6RkybMh4ul6KJu8njx2D3nbbCUWf8VXt++4a+vcDy83Lx1F1/w/c/LcD+s87HkIoy3HzVn3A9revKW0PP23Hm5qhraNLWNnbUcNzzj4sxd8FS7W8mMXztxafjnL/egm9/nIfykmLsOHNawm1I73vOqb/Hn/96i+aC3njZWbj77xfhkJP+ov33woI8vP/Z97jpjsfR6/PhyAP3wCO3XYFdDz1L29bEf647XxO8f/37ffh14TKMrhmKijLjhGqC8h4fv/0qbOjswtFnXqkJ5P1mnY+3nrwFvz/9cixYshI+w4k9bP/dcOFZx+Lym+7F3PlLMW3yJvjnlf+nCennX/sApxx7EPbebTuccfHftXXUDK3CiGF6BSa95twPn9RE+oeff4+AKDBgmIEKuWWllfrXTWvDIq1mQtj1ohnP2nM9mSuWCYlHU2yUQtfm751yHrM8bG2P84hQvuPibgX1vSQeVexe2td1i8Zno7M2Y6wRSm9WEegC2pM4HHxiPVqYOH3FIVoVCQGtKBS21r+uTcV5ZPGYmMX/8Toama8503Q7kCS169bjmn89CF8ggMUr1mDKhLE4fdYhEeLx829/wr2P65V/xM1X/gkvvfkxHnjyVe37ZSvX4oq/34cXH7wBl1x/F2qGVWHPnWdqAuTHX/R+ZRdcfTs+eeXuuOsgoUMi9IBZ56Ojo0tbz7JVa0P/vaOrC4FAQHMxE71Gbm6OJvxIVJGQuvyme/DobVfg+lsfwfom/Xdb2zdogioYDGLx8tX436ffYZdtt9D+Zlo7iav3PvkWHZ1dqF3bgIWLjQa6ccjxenH5jffgh7kLte/PveJW7W+dMW1TzJm7CL8uXK79E5Czu98eO2Dv3bbFw8++gU1Gj8DB++yiCWPhRq5cU9fnfaory3HP3y/G8lVrcfYl/0SvcZvb2KyfcJtb2yO2z4VnHotrbnkIb33wZcg9nLjJKBx/5L6aeKS/denKWs2FJdasNS5adG1t1qse29o7Em5zJssh0XHkuUDtEuCz8Gd4UFJerRfHdHfqQjFaPJJwEWIkEwUzosdjKGxtOp9T026fBXfAkng03r+nK7w9BHbNkg45jylWW4fyQuWtZ6Kp0npJN7DUyC3MdyWutDbPt5YnjlRcOELFL50Kpo7UF+BpChohayXpHEOPpPWIVkVCQFODcBp9SA3C65PQ/WL7uLU/hcSm/NzHjUI8DgRm/7wg4vvvf5qvhTldLpcmrogff42cdr7ZpHGYsulYHL7/bqGf0ZxNt9uthUfHj6nR3K+fTL9HIq2lLeokZWLqpE00d4ye47V4F7npuFGaaydC58S3c+Zp6xo/tiYkHklUir+NqF/fpIW7iU++moPVa+vx1ev348MvZuOjL77H/z7+Br6OrrjvS3/rHEMkm/9WWg+JR3JVScjtuctMVFeVw+N2Iy83BzXDdadh6uRNtJD5l9/PTfj3PXPPNfhh7iL86dKbNTe3PzeUUg3+ddU5mtsooG1BoXiCwv5P33MNPv3vPfjo89n436ff4uMvf0j4uswAY/gmwPgtgNGTgc/+i2xDzS9G0CmXTxTLNK+LdNhEg/ANrfaJo1R6PDbX649iLXaJ2XjikabNkHBcuyxmgYoqe/vQ3ybWkgXV1iJsrTmPXcDP2szosKuWSDzKdh43ywduGqOi2a/ijVH6i3qbVKxJMs3fZ+Rgyto80RN2RI/HuiQahBPdxroVtwKPosKY5iiVjUI8TjjHFyds7dacNfuK1eUi8gUFhfl5eOKFt7X8uWjIvSLxmCrdJsFnNyK3UaCqqiaWCXIb9znmXC20vdsOW+LCs2Zp+ZYUBm9tj0oeT5Irzz8Zu24/Q3MByTXs7unF/TdfouVbpvK3k0N6wJ47ajmgFGZPRGGB3t7jwmtvxw8/646oIGCMHfh5/hJsf8Cp2GOnrbHL9jO08P2nX/2I0y+6ydLfyWQhohE0TSmhquKOLHKRy6rgO/la1K1fDTyZOKdY6kzrprpIh03Q2QrF5ZIvjpKBHNGSysh1UQdousmlc5MYF+iEeKTv7zwP8MU4L4lm4jILZgoM15Fczq6OjIatKa+wokjRrglUab2k24V5XYAvGC46qfUlIx7lhImFWCv3ADMo39FwHpuSFY9+43ckbB4Famg9YhsMT6FND9EjLr0uIFcBi8dUchBpV/rdup2cDeJxy80nRXy/1eaTsGxlbYQzFw2JDgp/khCKxeJlqzVhRPmDImxNgrKsxDhZxWDeouVacQc9h8LW0VAuoRB48Vi0bJXWd5FcN+E+bjNjihbuXrJ8DZKFxBWFj+nfLfc8jXmfPYOdtpmuFQvFgv7WLaZO0FxG899K6xFrIJdPFPWQE0ntdUJ/OxUtuRTssPW0iCKaaKggqbOzG0/e/TetIGbhUv31RY6jefuQy0p5o2NqhuHlNz+O+5obOrrw6rufaf9ef+9zPH33NdrayTml/Mz+tjmT5YgcP4KKQ7JJPNLc5twC9FaMcOaEL5xHmtxCiLC1gJxHET51OmxNwp4EK4kzatUjoNC1K9ee9RTEEY9EvPnSdjizESFrNWMFMy4F+NcJ+nb2rlehBGj8HtCrKpqAnF6YeLpMRFhW0u6qNApdgrlAVXk45zFZ8dgrUVtXefWq86AK1EU5j2ubk3sN4TyqJB5dQIcNqfR8xXKImmFDtCphEjyH7rsrTj7mQDzwVF9H0cydD7+ImVtMwfWXnKH1Fhw3ejj22X077XtiyYo1+OCz7/GPv/4RW06biM2njNcKV7q64jtsr7z1CRoam/Hgvy/H1ltM1gpGqAp46+m6uKVKYfoZvR8VklB1cjQkknp6enHbtedh0vjRmnt43V/OwAtvfBQKWffHb3fZBqccc5D2PhRWpoIiqkanvykeJLLofcTfShXd3/04PyQmSYzvv+cO2mtuNnEs7rrxQrjIaTCgv41yEG+5+s9a1faoEUO1xuAH7b1zn/e69t8P4b9vfYLn7rseE8aO1H5Gfxtt29/stLVWJFRcpI9D+NfdT+FPJ/9O+3sor5JC80cdsidOP+4Q7b/TI+1zeh367wfttbNWSCQc1lW19Vq1fFVlmVahzgxASgznUYjHbIGcq+n62D3HKpujw9Ykmig8KzDnPDpdbS2KZbRQuhojLCtZXntzAE9O6u1x7Ki2tjpdhswYifvr4kNc2HWKC109Kkq/8KHVD6w3xM5PWui6/7C1KAiRtbsqjdfxVejXi5bGIFw+oNFwFPvDJ1HMjjDM5npqQWrkKoaKZZJ0HnuNdWvi0aZWjxuF8zgQeOH1D7XWL68/8S+topaE4xMvvp3wd8glPPzUS3HJ/x2Plx+6Sct3XL5qHV5999PQc8676lbcfNU5WgU1tYChyuyL/2i6kEVBOXxHn3Ulrj7/FDz8nyu0vEBy1i67US+yeeN/n2tFJs/ff4PmjIlWPWbIbTz27Ku0Vj1vPnlLRKueZKECkf323AHnn3kM8nJyNOF3zuW3YOGSlXF/h96HBPWdN16IYdWV+OaHX7QCIcHV/3pQa1v06iP/RFNLG+585AUUGQJPQIVGl/zpBK1ivbysRKt+vv3B52K+37W3PKSVuT1333WaA0lFL1f84z6cd/rRuOisY/H1D79qP3/q5Xe1tZ114mHaDQKlH8xftAL3P6nnvlHF9tknHa7lRpLbSi7x8f/3Ny1kQ1Ah1VUXnopZh+2NdQ2N3KpnoDuPFVkkHifP1OY2O+ryRTuPIkRcPVr/mlxZKqrR1uTAJSgnT3caSQBF93gUGEUzquy+ijTNhqD3jxWejkeo2tqb+bnWEp3HfWco+NO++nH4xIsB/NVHldb0na5wfuwAjqtKomDGJ9l5NDazv0JfR2u9fm5uTtF5dEmotg6FrE1/f6rOo88ctrbJImTx6BB+vx9X/+Nh/OW6u2IeWvEEAwmNY866Mu7rUoXuiedcE/GzF9/4MOL7ETMO6pMvSfl2sXJCKWwdKxcv+jXmL16B35+ut/OJhbllj+Cqf+o9DYlv5ujCKzpHNR4kYIWIFVXN0ZCzGL2mR57Vey0Kenp9+Nu/HtT+RfPld3NDf6e4WbvyH/dpglFAQpH+RfPyWx9r/2JB1eXmqvpoqOKc/jEbQc5jtjmPW+4R+lJmpWxCoSYKY0TOo+hpKMSjVjATcMYNHT0F+N15QGMt8Ph1fXs82jx+L26+Y3/Y4YSm4TzKmgi0r9F8+77/BdC6KACMhdamR6A7jyra/BRqVRx3Hv3lRnPwZj3OS43Lk6HXSCp0SRgHOFy06TH3eEzRedQLeBR2HhmGYQZM2DpbnMchNcAoU641FalQwzg7EeKMQrTmfD6z06eFrR2oth46Rm+fRIKWquG32z+BeLRpootl8WhDn0erPR4JI2yd7g3IeY8F8Nn8IF75VsW/9cYbWpsewWdtwDstwDftiY/T7pBYg9ywdaX+vhM69NdPZrqMuUDF7UrfeaTpMqobmHBMDh5aB1z8RCB151Gs262w88gwDJOVkEDIyQ1/X1IBlQRLtriOy34Gxm2uf60Vi9g4ZlRU9EYLFLNYMzmPtoXSqZfj7y8AcvP1ljwUJt/pYP29o9cTIdayw3lU7BhPaHW6jMQCHsrUeeFrXVxNyNMf9bC1To+q4IB5/e+DHp+8MDExxKtqgi1gzNrOM5zHpiRzHntFVN8tp8ejb4iCsqEu7DNUb1peUZSq8xhZMGMHKb/sdltN1ZpBz373EW1kHRUe9AcVJbzz9K1Y9s1L+PzVe7VK3cEEhWfNIVuGYTbCkDXNazYuzKpoV5MpqEhj2k7619+ZRm/aHboW+ZWdUaFRIda0NjEb7HUeSXAddaEePqdeig9fASybq2+TODmPil0FM+k6jzILZgrSCFvb0NR9gnF/ZQ5bJ0tYPMpZSwXdU9H4P5cCpUuFy6jvakrReXTJKJjJUREoDG+ToaUKvG4FgaCKulYLOY9KlohHan/yy8Jl2izkZKCKVhrzRtNT9jrqHG1aCk1Ood5+DMMwG03Imip4G/W2Wmqm8x5HbALkFQBtTcASU1squ/sqigKRziixRD0mQ3mQ1J7FRueR/nYq2qHQ+bM365Xe7zwC+E0VCC1Gg3C7xwFaFo9GtbUtrXqsOI9yc0JzFBWjDbPe7DwmS7fhCCoScx5FpbW3ORjKeU9aPPr1R0WG85gDBIr0Ffz32yBe/Ep3QZfWAUbr4KTD1nY6jylvepq/S/+S5YTf7auNgKPGzaI34bZbbqa1L+EpGwzDbDSV1q2Nep7f6MlQSbzUzs9sviNBzhvFCkmMkBCxu0BFOI9d7X2dx6duAtqb9O/tdB6F67tueVgoUej6i9eAXY/QG2TT6EQzWec8Sgpb098jXiutVj1yGyuOzdXS8bS50dSSJlW6JDuPQ7zhSuuOehVGfUryOY9+Q8y6JIlHw3lcuFbFrW8G8eyXwaRD1k45j7bnPG49fTI+/XpOxM8++nI2/nbhaXF/x00j+OI0TaZegMlsC2prE3o0WqJkEl4Pryfd9dD/WR0pKRuxDl4PhbuqQIaAq70RSmcb6LytVI7I2Hq0NVWP0tfUWKu14+qlsKPbA483F4qNa/IXlujv271Be98IVhsjWunnwaC+nTwe5Ehej79imL6GlvqINajfvo1AQRGUupXa+NCI3wnqXSfc3hy4JK7HX1CsrcXd09nnPRMSDBjbx2v5GFJz8+E79SatRZD781e0v53wdm9I+RhQVBWkoxS39fWYGZ1HWyWI1b1AjgXB7gsY52c3/b47bRe02B1Ak+E8LlwD6J19gQ2qGzlaXmVy63G50lsPzV0a5g2gzXAe17Uo2ut9a0wgzknypVWyHA3nsdDjQo4+5DppesXNVCbFY9WQcq2djBn6vqS4UJs7TCPkoinOy0N5QWR/PkGux4PSosI+o/zi4cmyyR28nsTweuKnixDVxYazkwF6qkbD3dkGj2l6SibXE4tMrKeuchjIxyr3dcLT3Q4KzLqHjMjo9lk7dDToDFnR1YLisjKsCAY0EVNRUoIc1b4RpfUlFaDW92VqAKVlRsueGLTleNFI5/OcPAxN8DxLa6geqa+hp73vGmYbvXWjfr5OUUFpbsWFRdr2ksW64nLtdcuVYEqvuyHHC8rKzMnJxXCL6+keOg5rjVB14ADDrAkGUZPrhpKT2mt25nq149rjzUGNhO2zWTEdnW1oUb2WXs/roWO4R5vdnO56qt0BqEojfEZF809rc7GH14/uIH1ehAeZmBxjPUhzPVXuANxKI/yG89jbW4SastRlWr6XrNkuLYdzWFE+atTUCviWNdKncwBWW7d3d6OzN3aH0Gdf+xBnnnAw7nj4ZXQkEJDk1NCF3x8MhpoxZxJeD6/HKjTj/KwTD8FHH32H+vZ2rTen06hF5fAd8mco69fA+8iVmvtAwihT64kmk+vxGYUIrXWroRg5j70lVZTZh4b2NsfXQ0err0wP3basXIS2lhYEqXo3F2jq7kGgxb7RiT6a7U3boqkeGxK9T6c+XalHVbFG8np8RfoFv612eeI1mAj06oK6rdevbS/Z26OlqT6l11U26GH/XiiWt0+wyrjxpdxPiu9SDmxHC2qbk+z3YsJVrofdfWmsx4w3T/9MrOgMWHq9+nbjb3MDa1ro77Eemy3LD+o5hl4F3b0qHl3Qi3OmAst6kPTaGjpcISc0nfVUFwS1zy+FrekV5qzcgDUWNvf6DWHnsaOrA2taLCSWZlo8Nqxv1saumaHvacJILNeRCKiqNic5FktWrcXT//0Apxx7gBbajndZpw2f6/Wgx+fPitnWvB5ej9V1BFUVz736IYKdvZoQSSakIB26INPnrbw64v0ztp44ZGQ9JXrOo5/y6qgghKaJeHPgLyqDr6XZ+fVQuxzKPVSD8Nev1vP5jLw3uvj77VxPnh4aDWxojXsOJ9w+/dwfVNwJn2e5TQ/tj8Z14VzGfnDRPqN1u1xy15Onjxv1b2hLei2Ex5hGQwUzlo+ffGPU6aoFwFsPATN2B+pXWno9j7G/VJdbyvFc5dYrP9b2qpZer71HSzLQ/j8YDMCvWhePJS411KJnSR3wc3sQe/7iQq0vufAt0dFrrEeh3UwpB9bWU+VREcyndAW9unrl+gD8FuZS69XoLu3/3WowNAFnQInH73+ajz12nhnxs12331L7uVVWrKnDbQ++mPA5lCdA9jHdOWTDxY3Xw+uRsZ6MkZevP5KTouUoZYPEzwJoe4iiCKq2VoNAcx1QNVJ3/1Yvc35NVTXhIhVRYSypT1/yrXr6meNs12xren/q7Uj7IbqXYyLs2j6hgpkU5lrLWo/ouUltk6hg58vXrb+W5FY9Q40RfOssFMsQncbvkbPmVQCjZ7jlSutAsS72ljfoL/RpP03K4xXwUK9Iryv5quhohnnDbXrWtVAurrXXEX0naT1Z0+eRcq+mThqn/SNG1QzVvq4ZpvfPuvRPJ+C2a88LPf+x59/GmJHD8NdzT8KEsSNx4u/3x0F77Yz7ntDn/jIMMwDIMcQjkRs7H9lxRoyPHAuYCcT7UwVvrxEaMkLXPsMBc5whRrp/w5rwz5yY6JKo2joKRdKs5LiV1tSiSLTfyZR4pNJbw3lMudraL6FVT2iWdYrC1YHtQyKJWJdgfnVyYk3RxGM6VHpU+A3nccV6ayq0S1RlU8GMkt50maBRLLO60boizsrZ1ltMnYAXH7gx9P3fLtRnMj/76vvaPOPqqgrUDDcasZJjXluH4//0N+15pxx7MNbWrceF19zObXoYZiBBbo6Acqd69Jy1jFFcAZxwhS7U7r8082162taHf9aUafFYE9lb0RBrqp0TXcRNhXj96D6PTjmPQjyS+5vpvooibEwuaHeKn5dQ30mvhGk/FpqC91mP3P01zHAe63xKWs4jRYrTFo/esPO4IgWz2kx3qK9iemLW3KZntdHVygr+0IQZJXta9Xz53VyMmHFQ3P9OAjLW7+x99Lmpr45hBivUImi/k/VedbPfzy7nkdyUqAkdjkOTQsjZIaHkpdv1QIYbhJuqE4XzWJoh8SjC1uvX2N/HMJbrSE25+3P97AoTpykepTqhomE69ZRMtehOxvYRfR2jp/1YINTnUdLNxzBDE6+16DyKJuFamDhNcTTEFLZeYYStU6XXaNVDTl8669Gmy0hwHkM5jtk0npBhGAeoHg1ssZve1DgbILcx1teZQggVIpOjAEtiOY/rtAdfpkLqwnk0h62dyHlMNt9RW49NE2ZC4jFqgky/6/HLHwdotUG4OayfznrizRm3gsSbD4+iosqbXs5jKKcvTbFGVJDzaAi2ZVbFo1/OemiudUg8NqURtjZPmMmW8YTMAIVO0kecC+wQ3zVmsghxIaYLkCfNKRMyyDH1CRN5XJlEXJiJ8mGZW4cQiFQsIzCml6g0S9lpyG2ibRMMhhxQjaANYdl4TlsyxSGh9cgWj9VZ5DxaF49SJswUSgxbSyyYqTb+JF8w+fF/8cYBwpN+zuPwclXrz+j3q6htSj/H0CsrbN1/q8Wk1pPDziOTFlUjgYlbAdvtn+mVMMlgFmhFGayyjpXzmA0FM0KoEDQKMBtGEwqMkK2aCdFPn3Mxu9kcOrZzlnSfYpkkxFJoPdkRtpbi9EkVj8a+s3oM0X4W7y8hbB12ZtM/psldI+p82pRzS6/Razq089PcZdXGZBmaZRJU08wxdFsXj26omrAWBTNr0nAewzmP7DwyspwjSuK28wLC2CAek5tyYCtmwZhtzmMmxaPIeTSHrY2egeTy2d7QiPI9T7sJOPyccA4o0RAulnEsbB0SK8k4jzbkYNJxKdZA4nkgO49GtbW+Jrf19yYHmjoBpIvYPhIGOIfyHS2GrCOcR7pnSdPgLzUmy9RarLSOzjGkVj1W2xcpuYBqqM81aRTMiDB6VlVbMwMUc9iRcmG6JNyNMoPHeYwomMmynMdMiUe6qBeX9Q1bG+JRu8jSc+zsEzp0LECjEOnfLoeF89zMxTKO5Tym0BomJNbc8l3H9ibAaGqdNHZsHxlha+H2pdrlOdSmh87zEm5hzAVptI1E/1ALDM3R11PXK0Ec0WGXphlaWK5oozutFsuYcwyp0NGqWKuhkLXhOta3quj2yQhbK8i1ySti8TgYxSOdWFg8ZjeizUe2iMfcLM55zJR4pGIZEogkVMyCSYhHceFPVcikQpFRUUvseFB4HVHikaplVdvFY/LOYyhMLHM9VotlbGvVIyFsTVhZk7lBuAzMYjZN8Sicx3UW2/QQVLyuBlUoLiUt8UihYm+pi6ZSY3G9hL6KsB5Gpx6PMoplzGFrIs9tT/yDw9aDVTwy2U22OY9Zl/NoEo8FxVAzIWipSXlMly9KPNpJYVk4PElCVrRn6RO2tqmvYizBklTBjA05mFaLZbLQeVRIHYVC+940imUkNAiPdh7T3GfhHo9prsnYZflpfMQqTG165tVJcB5hfT0jqU1PYfoh62hnNs+mUxCLx8EatmbiQ3OJdzkcHeO2yNwazGKomHMeE4pHciIy0a5n9GT9cWWMUavCfbS74lo4jz99os0tDl3ojXZBIeya6GI15zEk1mwIW2edeLQm4NIq4imQ1+NRW4vEPNV0p8sIqPd6ugUz5gbhS9JoXeuT4DxS2FoUy6xKo8djX+cRtsDicTDN4BWweIwPFafMuhzBHQ5C4w6HZG4d2eY8ZlvOo6i23tCSOfE4Zor+uHJeAvHokPNIc5xfvlPfHot/jAwz2hWWldHn0ZawdbaJRwth63RHOEbkPMpBkTQVKFwwk2YZsLGcvDR644wtAVRjnuAqU9qyFdSALvjy0hCPMtr0EFQ1TsEIItemjzznPA7KsLUpT4oJM3QMcOR5QEmF9q3qNW2zwZzzSOHQnNzsEY+0HrF9Vi8CJm8DlfIea2OIOLsgN5jek87Qqxb0/e9aTlgB1HT69KXiPFIjaBqLeOd5saftODHb2kKfx1AYVMaEoDRyHpVsFI9im1hyHiU2CBfrCQSgetLfRlRVnG7BDKEG0g9bTxyqizX/hvQKVIQTqrjTEY/h6TLptOkx35+5qNrapo88O4+DBfPFn3Me+0KC6NhLdOHY1mi/S9PverJIPJrzHbMhbG1+/9olmSmaEa7jumX6OL5MOY/i2DAc2LgizO6cRxKA4qYiFedRW5NHznYQ57WNzXlMK+dRYmGklBGFaqjPo9XpMtHLSUccjavSH7taZFSkI631RDqPcsQjkeO2p9Eji8fBgtlFM7c5YXSqx+iihNp8PP33UJNn2/v0xSPPlNOXVwiV+vlli3jMLbDc3FduM+oOoLE2M2Hr0Zvpjyvm2dPkOVlEFEGIx3jYnfMY6isYCE3YSb71SxpihAQrjfA8Xf/Moq0J6O3OvHikSI94rbTFoyeNFAI7wtbWt1GxGyhwyymYEc5jOgUhI4fo57HW5vTP9KohHq05jypGFABqnqi2Tns58BvryeGwNZMWHLZOjBGqxvq1kRdiOlHa2acvFvSewimmsCjFHrTcNhtbviST70iigC7WtB6zk+005kIEozCExKOaLfmOdKH1+4zWODaKR626OrnwpC1h2ZiV1iSU1BSrdz3Wi7hINIrzWd0K4H9PWnstWTmhOx+qz6UX7rivJ7J1k4U1WboBkd2qxwhbpyv2Rb5jmx/oDKZ3Aypy+tIRj0Mr9TU0SggTqwHrzmO5B8gtVUCfnpYOFRss3P9EI3YX5zwy6cHV1okpNsRje2NUTzM6M1EXMAcRFx66lW1t0HK5VMpt25BGOaAM55EuRJR/RRXE2hodFtWxwoFUKEJCJCcPATquW/px4GTNsy6r0t+Xci5j4UTYmtwlEpB0Fe1PJNgdtk6hxyOhXbLpc0Zizapgo8boJBzppuaNB4CF31tviC1DXJNo3OXwyJ+t151xK6Ql+G0JW6c/UlLGdJk+Yes0PmLlFbp4XJvGdJnozWNFrGkh6wJj0k0zpOAL0Oup8HKTcEZmzmPGwrHZ7jy2N/ed7uA0QjzSRZHWo4nH8syLR8rtozUV5UAl18cnqYdcOuKRztgkICuGwVdaBdQa7WqcaNGzdln8EKkT861FviMJR+oLmAg7CmY231nf9lQwlMp0GVMYVHP6rApacRxQodDC75AWoe3jTu+mQuRcfvqy3hw+VjFVkiihNXlT76whumtkWbX1MDFdRoZ4lOCsFRmjCVdq02XkOKG5FtYzktr0GOJxrYz8S1O7HrvC1pzzOBhzHumEHZ3HNtgRzqNRLBOaCpJR8dgBbGjOfNFMtHjMdMV1KOfRyCUzQte+UqNJtN2MEfmOv8Z/jnAe7QxbRxfLJELM25aV8zhsHHDg6cAR5+oFFBZ6GqbVikZCQUrEWsQs6XT2l+jHWr8K+OULXdBa7PGYVp9H4Tr29kidbiQj5zE0XaZXyXhYNj8HyDEKVBZbGEoUN2xtQVuPoEpr45S6VpLzKMSjlwtmmLSIzlHjvMc4YesmZwseYpFvFo96LpuaSfEoch57OvV/mZ4yE92M2qiu1ZxHJxg9JXGxjFNh62SLZSQ2eA4xamL4WKX8z1CBxgbnxIhE8SjFeYw+h0jLMUxVPMptEB5aj4SpQGK6TLqV1hHi0eJHbIxhFCs9KtbIOISCxno8SlrO4zpJzqMetibxCFvgsPVgzHkkMUInexlZuRtb2JqqNc1OTSba9QjnkaqJKWydLc4jhWhJ0NJ2yWS7nvzYzqOf8hDtgi6YE7cGttoTKK3UUxvi5TtqF36jYMaJsHUyvfxkT3Sp2TT89aZbhXMNU3Ha0g2DRh8H6SDDeTSnvkjActjahmIZWY3dh4acRwnLMTZPjibWUhdcmxg9Hj1tKtb7JawnoFgOE2tzrQvlisdeU5tQBar0DhksHgdb2JruiukOmU4wmcqhyzboAi+cE+EaOBF27M9R0ZxHY4KKmCSS6bC1cB0yGbaODpGGwtZV9jQQohuv468AqkeFLYbPXjYagWcybJ2C8yi72rpmQqR4XLPIUs5j9jmPaSXQSXYeLYatbWgQLst5HG7kPMpxHq2LNWITI8vF3aai0SfPefRaynlUwzmPksPWKjXHUIAeyYUOLB4HA4oSDltTgntxBVSuuO4bbqIcIZHTl06bjHQRwswsHrPBeaSQNR1LmvOYDeLREA3GNgrkFtpzQhu/hS4caX989y4w56P+3SXhXHvtDFsn7zyGQqAych7p80IuG4kJ+jvpa88U6+LRqhiRKR6F8xhqz+VPP3qRJpabhBcWy6+0jnBC03ce69IdTWjaZVbEGjF5mL4Gd1sQjVKcR2jkWDica3LD1dbynEdjG9OUGRfQI7k5Buc8Dra51iQeNSeLxWPMNj2mPn2ZE4+mi2JWFMwU9C2YyWTOY3RbGCHU7NpXY6fqjz9+olfRJhOWDGRZwYyMnD7BSCNkXbcSWPJTVJ/HdufCoNGFU+lgHpdodT2iYEZ8ZtPFqltsU9haSs6jzLC1KAixuLsmGGFrX4uKXjV9MRtMo7p5RH64QfhaSd3Gev1h5zHXhpAMi8fBlO9IvnqrMf2dnccwJcZJv8100s9kwUwM55HcvyD1V8zk8dPbFcp5zOiIwmjHyQgf25afOm6a/rj8l+R/x5dlBTMyw9YiZL1mMbBoduR/sxS2TtN5TOE94xLR29Vj7TMrbtJl5zymegwJY0DGdjGTZpqBCyqqJI0mlOE8jq42GoRL6PEYIWZTPJzzXSpKS/S1dPWqaE1iQFMy+EUTdpeiOY+yYfE4GDBf/I0Ql1Yww+gUV/ZxHsMFMxnMeaSCGSpSMWYna02wMxq2prXoZzatz2NGUCKdWUK4xC431LTm7saAxh5S/z56j1Xzk1+lEzcflgpmJIvHJXMip8VYCltnPudRoT6Z6YgjEb2gv9/qRJm4rYzcFnMeJTuPaTrF1V6AusYEVKBBhngU1cQWqptLC4DSItGmR1JfxaA18UgNwoOS2/QQPuE8uvWwtWw453EwIO6IKadPhDIKuFVPwirJUMFDBquthctH4iA3H/5MtVcy5zwKUZSpnEcxHjGG8xgSaxJ724VcRyoISeV17S6YoX0i5p0b7ZwSYlz403ZnafsOHRPeJpTGsGI+MG6q9T6P6TqPMsLWomk5CbV0xKOkYhltPVbzrrM0bN0VBM5bpqDEDQQllLb5LNYTEeMM19HVqWJBu5yYbiAURk+t+pva9MjOdzQXzNCm5rA1k57z6OsO3Y2qIqma6dsgPONh6yjxaIjazDmPWZTzKBxzWou4mJmLHWTvL5HvuCyFkLW2JsnHD12wZ+4VblAuimVofySq+haIEGi6ziw1BydxRaFykQIjQtfkkqfiuonyVCvOI53ThMiTJR7TcWdFvqOkkLWUPo+Sncd0w9atAQW3r3Ph+jUuqeLIingMVVq3BrFYD+xIq/72uFNvEB6eLgNpiLC1Xc4ji8fBQI7JeRQnFM55TOgaWL7rt0M8imriTLnFMXIeM9bnMWaem2qP00dzo4VYWz43td+VOZ6Q+hkefTGw1/HAYX/SBaBo09OR5NVGVtjaHLIWzP9GP6+kOIovXL3rtn4c+HqSE8/JkE5Y1k7nMZVjmrohhArKZIetJfcKlVQQkqpYi+7xuLhbkTvRxZPGaMJmec6jTziPNhXMcNh6MCAmhJAzIE4oeYXy88MGfMGM6cTvz4IJM5TzaKre9GeqQt7c59Fo1ZMx5zHUAzPKbaL9RftK5v4asYkeJidna93y1H5XlpilEPERfw7PTaZjg2ZrCwc2mWKZdF21WOLR3CCd0iruPj/lXL+0+jzKbBAeWk+WOY+hghlPap8PuulRg9ILZpR0nGIbEAEHK87jeFOPx0WSZmX4hROasvNoDlvLWYt5PVq1NTuPTFo5j3SXTmEu40ISEBdiDPYG4SV9XYNMNQknl0+IeiGQQs5jBsQjrUUcP1rY2hC0ObmZufmIV2EbateTIz9kvfxXgAoqUkHGzQcJ12Mv0YUjNUJfarTF2XTLlHo8RrpqbjlteszOI0H5oKluo3Ry6ELumkTxmI7AljxdxvJ6RKNyuvEkAZllfR5l0us3+jRaKJiZZPR4VFtVrOxxXjzuvpmCicP1r0fmmsPWMnMeTdXWnPPIpB12pBCfceEN5HHeY8gxMDcId6J3YDxEOJhCcUKAZDJsLVxHwlT5TQSFo+0k8RwnEbqUub9C4jHFfEdZ1dZVo/TjgW5qHrka+P5/4YkuoR6PyYpHkfOYxoWfRCzl09FrperEyq7elVwsk7YTKnm6jOWw9dDR+uP6NZBOuq2VJBMav2dhOWOMgpk1DaqU4h2zeOxv84yqBJ48x4PH/88TqrYOjyaE9LC1NmGGnUcm7ZxHwsh7ZOcxTpueTBbMmOdaCww3IyPV1qEbjx794kHukuE+BszC0iniiQbZ+4vcVhGiTTXfUZZzLQRiU51e6U4OKEUPSMRtMi2lsHWoYCad7VNifFaoUEZs70w5WXaIR6sTXSKK7uyotvakVtBESBD30sdJ2uQ8pno/NLQUyCMrLqhikaQ2PYQvyYKZmgr9eSMrFYwfqk+XCRqnUpk5j6Fqa24SzqQ915qcI8LIewwIF2cwE6tBeCbD1iLfUYSHTRfIjDh9QiBqrrWB4dBmxnmMLRqkTwQaNla/SJJQMqYypYQM5zq6jyO5q8sMIVs92lrBDGE13UDkWUoqxAjn0FkpmCmOnfua1nosOmvUMkl8bmVNlyFC60nhGBpuiMe1yyAbGRNm7CiYSXV3iWIZ9wYVCzsV6U6fu5/1lJjSxXeepKCKDmWXAn9ARUObXTmPkgdbs3gcbK16eqLEIzuPsUYTOtbkOZlK64iG5Z7MtumJFo+ZKJoJCZh2e8X+kBr9sWG1td+XIWZjjR9cGDXRJemwtamRt9WwY7xtbxUZzqPMohCrYXRxDqHPiPlz4nTOI4k6cVOxTr54tLR9yAk961960ZdN4jFV53ETc7GMvN0F4Tz29/EqM502fzNJAYyQdX2rZoZKXw8XzDASQo/dEWHrIIvHcCiuPUucRzE9JZZ4zETlt7lBuKBHX1swm8LWYhuJxtkbg3iMNX6QJrqYCyGSDVunO34vovl0exY4j1kUtrah0trSeoaM0F1QurmjVAfJWHJmqaVUWVX4RkgiPSJsTWNrMtymJ9J5TPyaJfnh/77NRJcp31GuOxgOW3PBDJN2zqMQj7pbwWFr04nf3CA8QrA57PaJyS3mnMeAafweteFwkhzTaMKsClu3xy6Yke08NtZa+nUpznUobG0SiCTcVpsqnZOttibBKUSn1aIZ2WHrdKqbbSmYsRgHDUUvbBKPyZ6DIvId1ewIW0u+4TDTY7Hv/SZGsYzMNj2pOI80GlFQVqygd7hLeoNwfT1I2CR8r1IVU/KtHycsHgcDHLZOomAmKtE9U03CQ30MTeLRPBbP6fUkynnMRNi6H+dR2vYhFyedqlWZBTPRoWkx0YWO0STFkyJhNrHssLWcPo/tWeQ8yiuW0daTalhf5DvaEbK2KvZtGpVodh4Vt7U2PYEWFWt65Vd/u1IQj0T3WLf0Nj2RrXr6FsyMyVXx1mZBfDs9iAPLrb0vi8fBVDAj3CMumEncIDyjYeuCuGFrDZl9DFNtEC7ozoKwdaeNBTP0HkK4rbfmPEaKWUVeziOx4Fu9+n3t0tReL91q2XzJLlI6BRjZ1KrHhukylsQaFXnZVCyjYSXNQAw26MgO8eh2ATWGX7CyIQhVUpueiOrvflRVqdHTsaVDF21qnvw2PdGteqKdx7FGMDLPBbwwKYjjhqTeEzQ7au4ZZ8LWNNua6BB9HouQHXVzDjN1R/3CTFW0sRqEm8VIpnIeIy6Kqj0TVFIKW5tzHvWvA06HrUmsxptnLLPAqdJwHen4EG59qphb2VDYMcXpK9rfKQRSdGia1nX/JZFucJJOlppNzmO29XkMZGnYOplzkN3FMlZbK0lOdTDTbUE8jqygj6MC+FUstNBEIRnnUUnSeXx/roojtguLV5ltevpr1VNu7MKACtDmeGRTFXmuIB6oT95PtHQWOemo/XHWiYejqrIcvy5chr/+/V7MmWsaVxXFqbMOxom/2w8jhlWhuaUNr//vC9z4n0fR05t+rzDGQsGM8ZiRAgyzg3bMX/Rcw7cfljefNhlhcPCZkT+LbhBOZLzaOsZ6MiEe80yjLTPtPAqnnPZXdJ9BI7QvZcJMmvmOfd1ib+riURTLxAtNR+fopiTWsqVVj8X10PYUN8Qyc+mstMaxM2ydivNYNVLfLnSsWGktZZczK5xHO8Sj8ZFSXIrmKAaSMM9GGD0W3R1yi2Uiqr9d/RXM6I8f/RLE4VsrUDz2OI8ibB3Leazw6EL1nRZgUbeCPw9X8fcxKh6qT75pesph64P33hlXXXAqbrn3aexzzLmaeHzqrmtQWR67gfFh++2Gy845Ebfc+wx2O/xsXPC327XXuORPJ6T61oxVvFFNwo0Lb0bFI405o3+bbQ+ceBXU8qHOvG9VTfiiQ2E/evz5k77Py9Rs61CfxzjVxBlzHmP1eXQ453H8dP2xOUYlqUyxL46RhjSmdNCFVoT5rLjXVKWaSiseJ5o8S2/VI8LWHmuuI4kr801Npgp47K62TqZgxuZ8R8tpBrJTHUx0mxoI5PSziY4dEsRuJSqqjeW4OlUslFgsQ/QGRfU3kgpbt7eryGkI2uY8+kzV1tETZiqM7bXep+Ci5Qra/ECpB5iWwik9ZfF4+vGH4qmX3sGz/30fi5auwl+uuwtd3T045tC9Yj5/5haT8e2ceXj5rY+xurYeH3/5A155+xNsOW1iqm/NpCsARNja5NLIr8lLErpTFlSOgO/4K9E1wpibaydlhkilOcE08u22PwLvPt73eU4JbHr9oy8G9j4+fp9HIlNh9FitejLiPCrAzL31L+d8mEDsS3AeK2vSHvGmF6ikIWhDs6sl2hHpjCik4ighqqQVzFgME9sQsrYsrkkpiNxUuwpmkjmmRaW1XfmOVtMMCotty3kUYWsiL8FHbEKeisc2VfH+1CBOMdJC3V3A4i7FlupvpT/xaJziCwNATl1YPNbJu0/s2yRciS0em/3QnMavjY/SjsXJK4KUziJejwfTp0zAHQ+9EPqZqqr49Os52Hr6pJi/892P83H4AbtjxrRNtdD26Jqh2HPnmXjhjRgXAAO3QjZ0erU8XuOEJB4zTabWoyoKfEaIxxvwQXG7oapBiCCaNycXinAkHcRfPQr0sXH98AHUqpFQR05E485HIGfBbHvft3KY/r6tDfAk2BeuYBB0LlA8Xlv3WXDsVPjHTQPGTYO7rQkBw3n09nZr+0rgC/g0oe/JyYXLxvVQAnlwhwOBpnVwL/gWvtwC7X3dvh64jfcN+rq1bUPiMdeh4zk4YQb8FcM04er99YuIbaOtO+DX96s3J+F+TYbeKj3n0dO8zvK2pmPGH/BD9ebCm5PXZ739ESgpB537lY5WKccfvUavIY48OTkp/11qUal+zujtQg5t6TTXROsRYk3xeFL6G4OFpfpnk44FScefth5DXLu83qSPIbWkUt8ufh+8PV0p7+dE6zH35vR6c8LiNga+EZvo54e6lbacHyL2lzu5/UXr8Rk55d7uDmnbRqxHc/qoq7ZLQWGuGx1xnMRxeXRm0EXaLsMAug12dalY4XMjJ8UekYnW0yNyMGmWdIK/tdS4564OKshZFwDdije0qQgGaT1SlmOcf8LV1vluJWJNlV7aOypagy7t5yQe9yoLYOcSBQ+td6PXPFRAhnisKC+Bx+NGQ2OkPb++sQUTxpqcJBPkOFaUleCVh/8OBQq8Xg8efe5N3P7g83HfpzgvD+UFckJi1cXZVVHs9HqC3lysML4ekZ8HV44bqssNMfl0SFk53BJDP8myZtgYkP85ZP1K5Cz8CquPugyB/BLbt8/aIcNBf21lzwYUlcVvXNublwPynRRPDmoSPC9dmsdOhvCWArsdGYp5DM/xwG163zXBoLa9SktKUWDjejpHTUHdLodrF66a1rVYV1Ck7yevO/S+vTkebdsEvXm27a8NE7aCe0ML8tfpFcVrt9tfEwyl879CRSGdGyLPD60eF8j3yc0rQHUa24eKgFYW6WHIGn8HXGm81krDeawqr0BuMLXilubKYdpxUeTrxhBJ+3uNcfGvtHAMdVcPx1q6YHR3Svs8bDAuULm5+RiewmtuGDIUlNWX29uV0u/1R7Mh1oryC1GZ5Ot2jJmCerpYtzViZJnc2fNB0wV8ROUQuOIUbwXdHqwwIjnDOxvhsen80C1uPrzJnRO1z5LhUtbkuKBIXpdP9UO7w3IBoytK4I3TA3dCEZ3x27DO74LHaNDt7wTchWWokVht3RPoCTmP8baPx62iIFd37sfn5cJb14FVn6u4Y2kBasrkRpV8xvFMfR7Lcr2oMR2fI/LJ5uxB0JuPmrICLFTpLN+CXUr1tS9rbMx8tfUOM6fhT6f8DpfdcA9m/7wAY0cNx7UXn46605pw6/3Pxvyd9u5udPb2pq286cJW394OXxIq2m4ytR5VJN4Hg6htbNA+KpoxTQ2DFRcaunoQaJecqdvfmqDAVzZM+7p5xYJQLmbQ47V9+/QW6ZWRzbXL0doS/+92u/Qio6DLjTUJnpcuvvLh4bCOSC4nsVS/FoppkojfEPgtvX4027ge/45T9S/cHqwZOwMBI2TW2NQQel9V9YRC+nbsL7ViGHx76GF89ycvQln6I/w1m2o5V51fvomuWMdrhx536VaR1v4K1gzRv2hrxNqGOilOVn1XN1wprsnv0aMFnU31Uo4/83oau7pTPoaCQ/R4X2BDi7T1FBhipCeopvSaASO3rGdDq7TPJq3Ha6yHRG13kq/r30oXbYEVv0o9T9B6qorD54PaDR1Q4oTpg8M30W86O9qwbs1yiXIocj0lVfrxQ7dEyfytaoVoEdeJ2qZG6esZV5EPhXrfA2jqbMOaOEtSvPp+/aZNhTfowlYAXltNf0OrXCfUb9QWuOJvn0ot40IXuUVdXVCKgQ8+8eG51VFpShLW4w8YFicZtP7eiDXlDdVvbJe1d2FNSy/ealPhHwlUV6m4ZFoLzngEcsVjU3Mb/P6AVmVtZkhlGRrWx04Wvvjs4/DiGx/iqZff1b6fv3gFCvLz8M8r/g+3PfCcFvaOJqCqCEi6INGFLRkL1ikcX4/IkevtjrzIU46YNxd+l9t5cV1WrVdL+nvhW782nFfn9qBX1beRbdvCSG730/smeB8KG2t4vPbuL5Gr9No9wJ7H6rmg3Z3wRVXnKkY1Ou0vWZ+NmNtnwpahbwNb/iaUb+WniTfifbt1F41CsrRtpO+v0qrwGnY9IpzrOP8b+OJUkrrFDYjbk972EWK+YXXa+13x+0P7LNGxFpMC/aYv0N4kbX+LsKOfHJpUXzNXT6dQO9ulfR7EelSXK7XXNJrTy1wLkWOsJ6gkF7bTGDlZ/50V86SfJxSSRST43R74SHDEe30Sj8Saxbaey8PjJD3J/a3GMUOi1o5zqJ9imQFdPLpddF2N/bxSt77uBh8wyUiXfWAZpK+px1Sg0mseB2qiQNPTLrR1qRibo69rUZdqz/YxbrJUtwIvIt+jzKi2bujVf07b7scOYMoUFw7czgU80v96Ukos9Pn9+GneYuy87fTwAaUo2HnbLfD9Twti/k5+Xi6C4qAzEN/T7zJOTZfpzo4CDHOxDDVgJnfNqQkqNGOVLpxUOdzfZApz5aVdxykJWUq2p4vWqoXAS7frFZvL52am+nuTzfUm5dQwnRpTU/86UZxgrrY2t1Wyo2m5ETbW1kH7QVT5fvNO/N8Ra0p3PaLS2mpzcBNpFcyEqq3luVlp9VW0YVKI5fXIrvq2Og6QPitDR+lfr5wPW0hmTeTKE2vit8vLSJ9HG6fLED7VaFSoVVvHP0cPMZbb5AeqjY9VXav8UtEuUcCjqSo14Vzr1k5gnGFULpXcMqhvtXWsVj3hbSL4ol2Bb4iNfR7ve/wV3Hrtefjx18X4Ye5CnDbrEM1JfOa//9P++23Xnod19Y248fbHtO/f++QbnH7coZg7fylm/7wQ40YPx0Vnz9J+Hi0qGTt7PPZkR+sXs3hsWK0/0kXWCKPrF3+5Fn6I8mr9sZmylPrB7PyRwLajD6VwDKglDL1+01rgrvPDLTFircfO/bXZdvrj/G/0BtQ7Hxb+b+aG1ObcK29OpLCUgWh9svgH4NevgP1PAWqXJJyoIm3CTLpjCWOJR3dOGtXWMlv1pDFL2g7BZnXCjF3V1qlWo4+cpJ+z6EZD4n6KIJlG4SMn6I+r7RWPKe8vGyutCb9Kx3T/rXoqjE3XYmqTU2/D7uoR4tGt/b+WjhmvQXhbp4rpIfEIWzACHwmrrSPFI3DMkOSFbMpnkVff/Uzr6XjRWbNQNaQcvyxYillnX4X1Tfodcs3wKgRNoWjKa6TQ9MV/PA7Dqiu10DcJx5vuiNEehZGPaKYbXRQjs7VJuuKR8Pn0tZIYsQvRSzJWn8B+mzzbIR5Few2TKIpTUUniSLVzf9HripD1vK9012+Hg8JCwywQVfPEGxvWY57YsWoBcO/F/f+OrD6PokG4DPEozt4pr0kJ57/KdB6t9lW0STxaFrO2tepJcT1jptjrOhL99Z4sqdQ/L/Q8O3s8WmllZLPz6Ff1sDWRr1UPx6bSCNH6jOyorl4V7TYItm5TaxxvnKblQjx2d0PrvdgThNT52mb84v2p+ttkKHoVFcXuvuLxJ7+CQKnNE2YefvYN7V8sjjz1sojvA4Gg1iCc/g0q6I70kLP0i9Bnr2R+rnVUpR7l0OliJAucR4LEWU6unAkhiXItiZb+nUetWIVOlnSXbdc2Es5jMr3ZhHi1K81gwha6S005hbWGmJ3/LTB1B/3GIzo3mY4n2i6iAb1MrEzskOGkUy6dEK7pTJdJN2xdUKRfoOkYlOjapDVhxoYxc5ablofEo+ywdSC1sPVoPd8RK+fBNvoLW9OgBaJuRepTjOw+fgrtaxBOaFvGEEj5CT5ilcamC+a7bOmnKOg2tcbxKtC6ekQjnM+g8R+X9+h9Fu3AF2fCjBhNSF2OWk1eRfVw/Unu9uQiwuk1U2TiM2wMMGU7gNqeiMkY2TCaMNPj9+hCQf36+jiPhri1Q4z0CVvXpZgD5bW3WCYZx8DuNAM6Vol5X4d/9t07uoCJJbZFo3k7nGIrEztk5PCKmxpyXSWE4i0fP6LpdOeGuE60JdIKW8sXAmEn1N3/OeysfwFHXRQ581uy85jSxBu60Rg62gHnsZ/jWuQ72h2ytiL28+3OeQw7jwUJPmIiROs26nfqbch3jJh4E2OiS7Tz6NISNu0LWSeabS22R4tWbBT+DzPG6l971ie3fVg82oU4wRH7nKRVpmZV2JrCxJkQjxXD9ZMPTSkxO0uhggevA2Hr+hRz1rz2rIUagpPoqV/V//PtFPt0bI6f0Vc8kgP5+HXAi7f1/R2/EPvZIh4lFMwMHRN2cSRg+fixY7pMuuMJ7ch5TLZgZsR4vdiNCrp+O8v+nMdkPmOjJurRpUYb8x3NqQ/ufpxHm4tlIm4+ks17dCLn0SiYySerLw6Vxu7MKVRsdR57AmE5VRAvy8AQj7lCPPbYVzTsM4nZXHfifEezePSuZ+cxs+SZxGPpEATMxQeZCFvHcR4dL5gJhawjc8oUUXFtV84jnehLhyQdtrbdeRT5jvUrk3OX7BT7w8bq272tsa9wWrNYD2VHEd5fkm+K6PXEiMYUwtZSCmaEkyRNPFp1HuXPtdbXI0M8ygxbJ+mEmufeb7Vn+PiwK+cxGWE02sh3XGGj69hfziN9VqpHhz+nNhNq1aOtx53xnEftaO7HeXRBRZmx1PwiIR5Ve50+xBePImxdZOxWW51Ho1VP9HrMownNbDlOf/6Dc9l5zCzGmDntgkw3bVvvhZ4hRluHTDiP0dMJMlUwExKPq+I4RzY5tCUV+gmY3ofCkhkXjyLfcWmSa7FRPIqLcypFIuJ4kr2/hOtIYeNUJh9JEY/CeVwJGVjeZyJsLbFYRluP1bA13ViImwSpYWvRN9Cd3PFp3h6UThE9/z3d9fRXnBIr33GV3eIxgXs9gpqDu4DW9am59Olun2S3kc05j1onzEDi2daU3+cyNFSJcf9jR6V1tHjMjycejaKdUsMxtatNT/R6KnModK2/Z7lRQGR2HoeXAUNLFfgDKi6bndyaWDzahbg7XvIj8MuX2oe8cYdDnF9HjnG0Rl2IRdNpx/s8xiqWsTuHznwBalkftwdX/JCRjc5jMsUydot9kYPatC7537Frf1kJWfcRjxZOyCRgxLFZJ4Z3Zqja2ibxGAoTp9oaRzhItM/jjMhLT6wlKR4//y+wcLb+NTWtjzFgQk7BTD/7i4YaiBsNO/MdYxXMUAGbcD0d6u8YwjTxqt9jiHrjivQCm8LW2pJC1daJi2Va/EBViWjTY4/zGFSNWdsJ1iNyHocEHcx5JGhkYk502LpvvuP8NUB3knVXto8nHLSE8nI6gNnvax96X/lQ5zd4KOcxnvOYJeLR7pxHUSyTZMjaVrePQuhD9XFvocrm/jC2j2qHkA2JxxTG8fltClub2/SkgthXVlsrUYseclPo80pOjgQsHz9ipKjkXDrLzqNNTblpZGpSBSoVQ8M3N798Aex7MlArP0ybdAEPhYrJ8aN0DtkCPxpzziMdowefpX///XtApdGTdLX9IWtCkxfGxJt+jyG6/tF5jgSn7MImE6rh4OXFWc4Q46PX6AeGltmb86hBh7Qr/npE2LrY2K3L5N2LxRSzgaAKt0vRKq5H5lKOZeycRyEe5yxPvvc2i0e7xSOFVgzXz5YLf9I5j12Zr7amqklKfI8RIqUcOlW2GNl8Z/0ET70Cy1Lo8Wh32JqS3EnUU2iWGoMngxD7Xm9WOI+Kr8fYXzmZb9NDRE8pSlU8CiepXk6+Y6Szlh3Oo+WCGRvyHSNzDF26UxXLSaSfixZb9Nmlz8x/74QtJPt5F05osp9dWWHralPa09Z7hb92ynlMRTwKt5qEo9mxlIw4pPO0gpm+x49ZKA017snsch4JbdZFgrC1KJhRelXU9QKdprxEu4pmtBkFJB5z6O9WQq16zDmPWxri8YflyW8bDlvbhTmpW4T4PDRhMktyHn0ZKJgRF2gSBtF3o7LGywnorvzA04Fj/gKMnJjadJk+1bKS77F2OlR/pOkpyYbe7Kr8pouz2DapXAyFWPNkR9g61JdTW5PXWmstifmOhEvss1QFtl3i0fI4wBJ7+iqaC8XirYmcaNqfJFqSzFW23ZkVN1sp3IhaXpM5bE2dKgiatiRcaYooJdOtQRbJurMi39HGkLW2HH/inEfRILwpAFSECmbsXBASrifUqqcXWB97/LVUxLxv1aVgpHEaEuKxyTg9UU7o9DHCeUxeobDzaBfmdhLCNSIbn05MNg6vz+o+j6Mm6Y+rFtmfQycEEW3vw/8U3geZdh5JyI6bql8Mv3g1+bXYVW1NEypIANL2MYq7kiK0v2wKW29IvQBAm8KTQ03dLRxDompVUr6jWE92teqxOtHFrrC16TyoiRFfglzlelsdrAhxnWwBTyppHlYxu9eVw8PN+3/+DNj1cD3tRWYv0KTXk6TzaFuxjA796bS3cj2Jcx7bjUaHPT4VzTZNvxU5mPROeTFmbdN9ekl+2HmMrna2u9cjha2JiqiCmbFVQHG+gs4eFQtT8A9YPNrtPFLY2hxC0y4kNiY6xA1bR+c82uQcJWK0EI/zE+TQ5ch1sMw5ZCnnPNogHqlpPPHjJ6mJtZDYl7y/Ii7OKfjisvdX9H6z4DLR/rI2NUkJu+LrbAhbp7IeutkT0QLJrXrCfRVTLZixRzwm5Tym2Js1vfWkGLZuTqHAzCrm6IdwHqm3JKUQvP0IHCdZ59HmNj3mQzqheDR2ZXeeLuYa7F0OQgU8MdZTlAst/1A4j06IR9HrUQ2Frc2tevS1VJfqj7XNoXqfpOCwtd2tesh5pIuIuGu2I2fNQpNwy66IVehkI6oDKQcxXt9AWeJIOFhU7S4uepSgH6NnYf8FDznynNexm+nHw5evpfa7dhUUWam0NnIe7RGPaTiPVvdXeZVeQUvbOMXtkNx6vKmHrCm3T2Jlc1qzrW0SAgrdrPTXrieVefTpridZV81J59FcsV9p7bMqlWS3UaFD4pE6hZN4jPMRE0LJn29vpbVANdSXuSl3dL6j369qk3GEeLMTn9l5jA5bG7uywgiSNm1IbduweLQFJdwkXPQiy1RfRRG29nU7V4ARC3J2aC0kpqMahOvrkSwei8rDY7tevkP/e9emFuKxXPDQr+v4cWquo3k8oTs7xKMt4yRJQIiLjhXn0WoHAeE6UgcAiSFAS86j6EZgg9OWddXWyYgRc6W1zYTC1onWQ+cVuiGn40RSVX5S24cEK33W6PsUboDlrydZ59He6TKh5RgCLCdu2FoXRKoh3Oocch7zYqynrCDyUuyI8xjKeTSHrSPFY7nhc6UazuewtR2Qi0EVhAS1/iDowkYf/kyJxzitehyrAA/lOy6M3WdR9oQZc+HFynnA3RekPK9YytQSwR5HA2Om6Ns9hVxH28W+lTY92npEEZjE45nSCygvmC6QFoSK5TQDG0LWlvs8innnSTaPd6ZgxkbxqAnanKwIW0fcOGi56f74YpaEoxO5hmINotKatoOTOY4x3Gs1i3IeA0arnpwYOYbmnEd3oTPOY9Aw0nO88dv0+Hv0NTQ7sBtDzqNbQbVXRb4rPHEn7Dzq62pKsaMSi0c7Q9Yk2EToKnSxdTJsrZjEY1dm+zwmynfU1iO5YCa65YuFytWUJk7EfREF2PcPwIzd9e/ff8rSNAjbmrqHxGOKbUd6bXAeaQpQaF+p1qubrYpHSWMJ05ptLZrHr0uyeXwqBNPNeWxz2Mkyt+lxwHk0z27uT8w6FDoOnYPM+Y6ZJMvC1uL+LG7Y2vh5rphrbXNbzqBxOMfKwRRha7Un9mxpO3Meu1XtFg1TydcydLYQr2HnkcPWmScUst6Q2abcZiGWyYIZElAjJ8XNd4wcdyc5bJ3G2C4pBTMHnakLR7olff1+vWG8Few4fuiCXVplLefRjuMntM+stWRJO2wtXTym4zzKF4+Wb4bsdJEShdLpBpDOYbTu1hTTPCznYPbjzjqYg6khbkBEJCuT+Y5WCmZsDluLamKvsbt+W6picn5YBA0xfl5o81zr6M1DTR+IicOB6pLINj1Uae102Hq94YhOL9Tfu80P+GkiDjuP2Vos0xHZRoS+cDJsLVxHKtaJapps66zkWHlctE2oaCdOaDB84ZewfeiCI/aBhcILaeKxapQ+Toxe5793Awu+Tf8iItPpo4btdFGicH6q00xEmoEoyJLpPFoU/JYKZihUTkUqJO4l98tL+TNG7aXouKXPavQEJkebhCvAHkfpAmHeN3oajm05jwnWVG4KEdvcpifCyqIrf9wcTOd6PGpEh84HivNop1sdw3kk8TguV8XbmwWxpBuY9AOpNzUUtg7NtbY55zEYDDuPNWUq3r/ci8YmFTOuDKDUKNpxG6dOJwtmGlVgE5qbUdDX9Qw5jykWzLB4tLVNzwbnxu+lMppQW4+D4nHU5HDxSryLgMzqXVGxS2I1xTxHqQJbTNMhRysd4UiIPo+J8rFSJZ0LobG/pOY8pukWWxL7wnWksH2qU2lkr0e4jiRibchrS7raevRkYLv99a+32Ud/pL+lp9NZJ8vhELG+HtpnuUmErR0Sj+I8Lci085jMPHLaduIaaLN49JnE40TjHmd8HlDlUdEVBHIMw7a8xHnn8eAJClweBVXVCsYPDYScR6/PQefReI/mgP73bz8BCChKRIhaVFunWjDDYWu7G4TbWWDQHyJ00B3jpO9kDuaofvIdzeuR4axJCFlLcR4tTktJanZzJiut7erzaHU0YTpif6j8yTKW22ENty9knZJrNHJC+LgVDrNNTlvCULooTnHK5et3G5mmMTnR49G8HkGjAyMRE5GMey1cR1p7rGuPRKjtjRCPI4w+hsQWheEej10qMEQ4j3ZOl4G5gAeYaHgHxG5TlFDOY57PwZxHY3e1qECgUMHoI3LQvKc3QriWc9g6G8WjScqHJoQ4KB7F1Iz1q+P7/XZXW1O+oyiWWRkn35EI9Xn0ZlyEpFXwELEOEYaVMFZN9AqlamQ6htJwVEOUW3ceQ305ZYbR0xTbllIfbMp3TMt5tEk8hsPW/eSriX6sX70B/PwpMG5zW8Lo/TYud7LSOrSeBOfF4jJTu5z1zq5H5A+K1m+ZIlTg5Ok/0iTjpjlJcUTiscb0sd+qRMUJR3rQNMqDnoAKl0tBIKii0d7ibwQMh4/C1kPLwmHpPaa40GS06Mk3BK+TE2ZIM/tpPYqCQIk+rlFgtWCGxaPd02UyGbYeOjr+hdHugpmxU/V8v/Ez9Mo7er8EF0WpBRiSHD9LrVZi5fBJmMmriAkqtH2kO48W3AzZBU4yxKOlAhU7xaNpOgiJ/oR5ewowbKxtbXr09SSZ81hjOI9rFus3KfO/gW0kKphx2uXrLywrbrYczMEMHUPZkO8Ysb/c8c2CnQ/Rv/7xI9uX0yvuF90KRphORfvu4MakbdzawEsRXl1al9oEFSv4DefR61YwrDwsHredqODbpfp/cxmnTifEY6/xHm1BBQHD06Jy6w6vkX/pAsqMSnR2HrM0bE0nAWuj09IgQUhOag/DaGbuBex1fPh7EtFfvhEZeo1GppMVcvzSFI+hE2UWOI+i6EoTjzmZD1tH9OWkk49qfRtR0Q5dkNMNW6fqPFIhiHC37BCP5nw1+pwlmhhDc4tpPZSfbFNoMqkm4XRM0PmL9q8N2ySuWOvjZFGI2PmwdWjEZbaF0bMhZG3cgCTs87jZDkDlCP3a9917tq+n16cLn4JCYFSRfg4KeoBNd9DFbcEvfixaE8TtDW58sdB+wR8wOaHlJuexMF/BNuPD1dZU7RzQzpvOOLNkuAaM8DTRa4xrLDN8LqI1xQwDFo9OOY9Oh63J6aBqX6I+Rj6XXeJxi93CwvHnz/SwFzUG768AwOzM0t1rKnOWoxEiJI1KaykCW6LzKL1CnkSfWJ+V5H9/T+RrWRmlR1XOZ/xdv2GgiTvis2GhJ6el7SM+H9QGxpyfbEffwP7E43DDdSTBZpOrlVTBjAhZU59JJ5pRxwulZyJE3F/YOhMFPCL6ob1v5sVjwgInuubsfKj+9ddv9hmJawetjSrcbUHklrgw+bdeqF/60LmZG658Bc2NQVR/78fS9cDjC+0XambxSAUzBcbMaHd7EIFiFwrzwnOtnch3NIetO6LEo99oWG4OWQdSPO1wwYzdc637FIQ45DySg0AXdfoAx7pTDjWdppCapA8Whan3+0P45PH6fcCKecldhISTJUNgF2dbwYws8Sih76QIBx51cVioWcmjMld/Wy2aGb5J2GkuqdQfSURarCRPWezbGLLW1kM3QMnus2E2NgdPVAxC+84slETImjojOEG8nMfRU8KhWqfa9PQbts6E82gOW2eBeEy0fabtqF93qML6+/85shyqJi791IdgUEXBRDe6JrvROVU/vhu/9kNRgUbDnXRSrOVRwb4RDs5bFHn9I+fRieky5mprOop8xno0jMp00eOxxcIlgJ1Hx6qt0yy+sJrvSG0/Yrl40dW7ZvFmBeqXd8Bp+t0nNcL+4JnUfj8ixGfRyZLUbFqK00fHQJpOmvQcTOEM//Y4vY0T5bO9/Yi1tUDV8lS1MLomAC1kog8ZoT9STt0PH+rCJV4T+WTWFBJqORkvlok4rkms9bfP7K60jlUwQxGSs/6lC7THr9XPEyM3Dec7OoFwZ6Pd0Ekz9ceFs+EoiZxHp3s8mteTLc5jvGp0ciJ3MnIdv3LGdSSo6413vYofPg1g6908aN9O32+epiCmrg1qCscpl88sHisqDaHmU5G3PIiOrRCCnEcn8h2J3lAYXYG/SAm5hR5NSKoh57EpxR6P2mtIXCfTZ8JMrIIZh8LW4sIYK2QdU6z1pt8InE4oFNJ557E0xUgOYLWYmFzUolK5zqMVwS/yHan5toyejDLC1uQ4kjNMAn/5r8Ab9+tOn9X1RIhHC1BulBBvy3/R/zm5v5wSj5TLmGhNtD/EWmwqlulbMKMAQ2qAvAJdtE/dEVj0Q1jQr8mg80jH0ybT9a/T7Y+a8nri5YWaRiU61ePRvB46jpwM36catqZwNTmzVBE+2xnXkTBaJqLx2wC8k13wDdXlUdEP/tCIwEYHxaPPeK/KCiNk3aHC3a6is0VFgZEDqfSojolHIWbLCqCF8gV5RZHisdlC1s5GG7ZW8woRTGcmsQ0TZqT3xbNaaZ1qSC2VEG1Lg+XiifA2yk3PAaUTG53kUp2aIlOsSc53jFiP2+IxRAKFhAq5W0//PS3hqK0nomjGAiReiPVyHBVXqPdkEvuLzg3i/e0Uj8lMvakepR/z5ATbKEwiZze7wxESYufDgFET9eODnDU7psnEWlMssTZ+un5M0TokT/2xHJalhv+hUYnOiTiFQtXr1wBzP3c2fB+PWPuLbj52OEj/+t1H0zciLIjHMbmqFr5WN6j4dX4AOavD28pJ8eg33jbPEIruDapWFtO5SldxqqpC8TkzXcZcMDOuOvL9CoqiRhNaCFtvnOKxsBS+M29G3X5nOP/eNBJQ3JVlcsJMdf/Nj6Xl0EmqLA6160lHYIt1bGhNr+gm3TCx5ErriPVYzZsVTl8DXZDT71nhSmukpKJXGEtsQZLS8UzCkS6AlFqSpohOSDJ5mNTWitBC9jb2EjHnHtPfbhaP5ErveayzIet4TpYIWc932HUk4rnXuxymP9YudVTEaefE+y8F3noIWUH0/qKbnoPO0DsmkMB1eJ+FxaPu8i17pAfn3xdZx9zkYM6jzzh8FKMVDtqN9jy1+jHj69Z7UzjlPIr1bDI0chuIcY3lYrqMhbD1xikex0zRRFxvhXFxchJxQqacPXNo2PjakQkzVMVKvRVp0GaC5r5S50lLEEvpiRG5ldZpt+qxQzym27Q8JNbkOH0hsW9lvjU5s3SjRRdrSTlkMWdbkxARIWGnQ9bJFvGMM8Tjsrn2riVaPIpJIKLhvMjpc6pYJpaTRcc29YYlFn4Hp4nphJK4n7aTLhrffxKDmT69Qn87Sw9Xkxv7burpSuli9NvGSOMjv6YXmN8F9Jj0vaNh60Dk93XN+gJL1gbx62oV6xboC3NMPBrrGWXUI7o69fVUGOMaKyz2eNReCxsjI8Y7N3ovmekyEdXNXudC1v3M65Xa+iUUprUu2qSE9oV4lNGYOx3nUcL2kL6/KmwSj55c6y4oTQ+R1BKmz/4iN+3wc/R/GRKP6G+f0c/F+E67xSM58ebxcvnF4ZZa1K5I4KTzGC3WSEhTjii5wbU2Vp4nux7aP/ucqH9NFcQ2FjQNCEJ9Od36+WTG7rqofu1eOVOvUsSnGuFhw1ir7VW0n/1qWkomch4FC40Mh2oV2Os6H9Z+qJ8PnCri8RlNy6mJOuFt0MVrVUmU85jidJmNVzwa7SbI5dMzDjLR4zFKyoeEkdfBkHXiC6MrnYIQG9rSSJkyUyTReRQXfgrJxOpr5mCbHkvVxNEIZ6lRdo6hhfWIwgzK57LLmRUFDpSvRoUhmRCP/XVZGDlR3590s+PEBBGzOCooCheWff6K/jUJALtGESYTBp20jf64gFxHm8eBJFqPEI87Hqx/buhz/PELzq8n2zBvn9HGGEJqx5ZGlwQZYWtBrXFK+rFDyYh47I26D5691pg44wLKqXG4cVg5lvMY9bcL8ZjrVbQimgrjFMDOozioxUxnwqm+iv04j1JDxEkXy8TPd5RexCMl51FCwYykHo/aesxV0qm6fTaErcNhfa81UU2ODp38W+TMCk5rf0nOd4wQj+J4Ligxvd8I8zP1IpUkPiNp098+GzdNf1xur+sYwhx2DJ2r2oGfPgU+fRl44wGH+yqa1kMCctMtTeIxA4TEvkcvvtv+AP37d59wrP1MVmO++RBtnZxMc+hHPFLYmvjJuPzSOMKWDLTqEfxQF37/6gjxiIyE0W/6BWgzXMahZdQkXBexzVwwYzgK5hO1lZCa7OkyThfMJOmqpJ1DJ6C/SeRPpSHaXAEJ20jSaMI+4jHVbWRntbWV7SPEmtQwcRrOoxBzMsWjCFuLfUV5v9HvJ8bMkZCmqlC7Gy8HkhSPy9JrU2Qp7CjC1lRZTYLxs5edb41jdh6p1yUJWlrP6oXOriO0HpM4omk79Eg3GBnIv8xKzPsrNAM9e8Qjha2JHzuNXD4/EHQw+tgrkjCJgIqfG4E64xQwNMckHp1qEh71Pq+sBGqNtsPVpUrIeeSwNSEOaIFTrXESTZcxT5ix23mkIgTK9UrU4zGeU5NuqJiKhKxMK8lW55HCZiHnyJPaDYT4GySsQ4p7LblYJjJsnZtG2Fqm8xglZsk5iv77zTdXVHVus8uWcJ+RMyrW4pTzaBZH4obPhtGMlpwsc6/LNDslWCZ0A+IJTyCysffmgEPsL7o5pnA+fX7WLMnYcsxazRy2/qwNeKJBwfWrnU1b6zX5Da4NKtb0AA3GKaDKA1QYlxHHch6j3md1I1Dfpm+04WVAaYH1sLVnYy2WCeFUU+7oBuF9xKNNs6SjocpA6tVGCfD99GqTMrFE5jhAKa16JOca0smStk8q20i4nzSmyzzJJ03Saq0kxJPEKRWWxT65SyKkLHM90Tm8EeLR5DyKtJZ1Nuc79ve5Fy16aB0O9VWMEGuxJmFlsrpZiEcn9kt/4ppuFsVnxu682IGE2F/DjFnsDWuAns6sC1tTs56TFjtc7wCgx+T0+dtVbR3CeaRelPmuzIWtWzpUtHcD9Ub74wnDFLhd1scTujZ28ag67jzGmC5DiMapdotZqn4jfv2y36dKC1tLCtGmnYNJoUhyXmU6flbGSpaIim95rmPcVjQZdB4VMUIy1f0lhBw1lJfYUDh8/MQKWw93bKZ10uJRtOhxynU05xjSPhPnKqeEa39hUKeKmJJpRUPjErNgPVlHdIFTBkPW0eKRcgs7g84LRjM9pkKY9lZ9cfVGn8mJ+WG3tC0DYWtyHYl1Lfq6Jtfo62rrUvuEtwef80hOA1VWkpVOY5Ko36HTzmOM6TLS2+LEo3QIsMnm+tc/ftTv06WFrSUVh4TCoFbzVKnfmNj2CVoUpYQVwWZDsUzSPQMdatOTVl9OMdlFcnVx6Hgm550ubgUm55Eqr+kGgJ7jpCiIvvmgcxKthdzacZs70qIn2ulTza4snSvTSDWR5mTR9qhyYOJPv+sx9ldppR7FoO3TT/rPYCIkrjPR1qkf8Shcx0zSa3IUGw3voN44pCbn64vVC2icr7Ze1WSI2TZjPSOMYhmLgQfPRuk6rq+FEgxCpRO1085jaK51vLC1jeuZvqt+4aTkeyqMsHN2sw1iqY9zlCqTt9UfV82HNJIVbCRIKExNjqdd4tFq2Jo+A3RjIT1sbbFJuA35jjGr4wuNnD7RbolygUkokXDSGug7MPrOfNNIYuSMf0aek2gbOlkcIsKydG4kujszO/ZOOFnVI/VzI61HG3GaIYQ4Gr6J/ti4ztFxe1mPecQlkanCphjicW0W7KYe0+ZZI8SacQqYlO9svmNf59FYj+GI1hjzt5ssFMtYDlufdNT++PrNB7D06xfx+uM3Y8Y0o2Q/DiXFhbjh0jPxw3uPYtk3L+HT/96DPXbeGtIZsUn4bkhGz0CZBTPiBGSlZ2AykGjcYlf96zkfJvcrsnpPSst59Fl3HhUFmLZjuOmxk24fhWFPuho48Wo9dG5DpXVa7nX5sHB4UmJ+m+Um4TZUWvcRj+RkibxKsR9ItArXkd7bPAHKLszHz4QtdeFIAmndcv3C+7+nnFlHtDgSRW6ZLJYhxD4Tx4Tm8qkZXI+xL0RIn0PWkZidR4ruJWFS2InfaBJOrDEqrTNJj6mCZ0mD/nWdEbauMk7bmWodtMoIW9cZOY8Cx5zHg/feGVddcCouuf5OzP55IU6bdTCeuusa7HLImWhsjloVncM9Hjxzz7VY39SC0y+6CWvrGzFyeDXa2m04aY0wKq1rl+jhay3nMUOteuJNmBEXkuhuoukyfgvd8aIP9MLvk/qV7Mt5TKNgZsxm+t9PF8MlP0IayQi2qTvoNwQkonc5PPvC1jbkO6bVJLzSJudR3KTReshZE42eqYEx7SN6X1HF65AoiNhnoi3P128AX7yGjIq14rLM5zsS0WHQTIu16PWQyGdiO48ZzneMdh5FpXUm6TEdPvMNXS2qrQVOtemJDqOvinIeQ+uxKMVSdh5PP/5QPPXSO3j2v+9j0dJV+Mt1d6GruwfHHLpXzOcffehvUVZShJPPux7fzpmH1bX1+Or7ufh1oeQPJTlPwnkk8SijcjetgpmoPRIRUrNhTVv+Rn/8+dOk+/ilVb1rZ86jlX1Gs2eJeV9HbmuZrTviMWW78Ndb7wUMG2dz2NqiWJMYsrZcbU3PpXwywo6JKuIYEmF6upkQOWu0HZwughA3H1TIJaqrlzpYINNnPf4o5zHD4jGYbeLRn13ryTbM4jqDzcGzVTx2i8MnqOInw+kTYWtBk0PTZfqErdersZ1Hi2HrlJxHchGnT5mAOx4Kj2lSVRWffj0HW0835rNGsffu2+H7n+ZrYet9dt8Ojc1tePmtj3Hnwy8iSHlHMXArVEKemq5VK0fARyfo3i54m9ch6PeBtps7Jw9utw1h4lhryMmDzxAZ3t5uKKb39brd8Pt7tT6P3tw8KD3yktQpt9O3yXT9fX7+NOJ940HrERd/V04uPBa3kepyw2dUtXo7W5N677jrMS60ijdX+z7pNXhz4TPGmnl++QIuCftbvL8S1AsM3Dm5MY+jYPVo+Knfma8XyspfoY6fEUpd8HZY3x4Jtw8dQym8rn/IcNAnzd28TtpnQVuPkYqRyv4KDhsL7fy6oRU5vm5A4nrMBSHu8mrt8095qJ7mOu09lSEjoOboiUeehtVSjpP+1uMOBvR1jJqspzR0tsPbsEracZHqelxiPUbOo6urw/JnX8Z6PGpQX4+B3ful//UY20f8fP1qx/eVeT2pfM4d2T5Q9c8vfb12SUb2lXk9KqVrGdT73chxuzK6noYWt9Yc3NuoYk0vrUdBM4250c7AOm0BBTk2b7fQcaOGt0ddK61H1/9UYV2Sr4vY1s6+6+mNduDTFY8V5SXweNxoEGVEBusbWzBh7MiYvzOmZhh22mY6Xn7zIxz3f3/DuFHDccNlZ8HrceOWe5+J+TvFeXkoL4iaRdsPnTVjUUc3+K3rUVNaigZFBXl/hYXFKCszQjQ24ysqB02FJVFWU1QABZF/wwq/TxOP1RWVyHHJ867bJu+ARpcLuXXLMULtAZL8e1sNMVKQX4gqi9vIX1iGVfQBDvhRk+OBkmN9W3cYYjYnrwAjUlhP+6YzsZ4EcEs9RnY1QZG4v3OhoovSBotLURzjdZu22AV0I1ewah4qv3oFq0dPCaVKjHAF4ZK4li5j+3hyc1GTwuuuqRoJknlDuttRIHE93YbL587NS3o9LZtOB509ChpWYKgNn0t30K9d3AqrRoCKCvN6OjHEt0H7XGrOo+Gyj+hphduB80KJx4319EWRXt1cWLsQ1WWmKnCHyXUpoK58SnG5JrKLVR8qHDo/xqI014tQ1lzAj5H+Dqmf35TXk5MTWo+7vQkj87xAXubWU11sKvrKAirycqDFC+h8390i9fxmhaJ8usbq5yFfbjFqyhweRxyFqhZgyIvN6OxWUFxcBX3vqegNNiDH0HF+D50vjQilzeR7ycjoQFsnUJJbhhIjSNS8YQNK8nVBqwYLUFMWGc1a1mjYppmstlZcChqbWnHRtXdqTuPP85ZgWHUlzjrx8Ljisb27G529qXnQgXG6o+BrqsOalhYEO3Vnb0NQRUeLMY/HZgJD9cIhdUMLaqPe0+wc1XV2wyVxTb7h+vv6F3yv/e3JQOvJM8RIZ1BN+veiCRYY4cH2ZtS2WC+YofUUGWKkV3GltB7fuBnao/rzZ322ezrroRN3bzdJR6C514e2qNemi69vnO749vz8OerWrIDr81cQ2P0ozWFa2yivapTWU1ql3/P7FHfS20eFAl+Jnv/btGoxmiVun9JK/fjxu71Jr8c3RA8b9yyda/mYS7S/Ar1678n2XP3k3NvWhLqVS7SLnSrSM1rXY12dDSHzGOtp74gMC3cv/EHq353qenp69PnM4gZnQ3MjujK4ntYN4fQepWE1apvlpnqkvp628Llt7bKM7Cvzeurb2+FLwgVyaj0tKxdDWfoTlLXLsLZxfcbXU9+hn5+JH5s2YK1RnJKp9Xze2IU31ij4tiPyGkaNwkcZwm1lRw/W2Fw1I9bzzdIOPPqRirmrIq/xtc0KxlTp22pZQyfWtKTe6D0l8djU3Aa/P4CqSiNfxmBIZRka1scWDvUNzfD7/REh6kXLVmNoVYUWBveJnDITAVVFINUPjFG0obau1yxXlzHEPuD2IujUh2/b/fTHuZ/FtH1FmNhPxRWy1kRhcioWob918ZyUtlu+kd8TdHuTsqljIvrFtTdZf42ovoHkzib9WiWVwJgp2peBnz9L/bjpB9UQtAGXu+9rU24j9ezr7UFg0Wz9v3/9NuDN19rApLs9EhVfJP3aVDhGrXQCfu3GStZca309RpPwZPcX5SWP1G90AivmSd9XGuIYouOCju0NrVoKC5rWAVVGdGTdcun7Jh5CzIa+X/KTPX93kqhRld2BjtaMridgaoOjOrhf4q/Hl1XrIeGY6TWY8ft6oT57s3bjnAUphug2yokDKrC6m1IOMltx3R0I4vAFIlQciCkeG3qD0utl49HrD+KyZ/q+2ToKrxvbqqGd1mPzbGsSej/NW4ydt9XdFkJRFOy87Rb4/qcFMX/n2x9/xdjRw7XnCTYZMwLr6htjCkfLiAT5VsPtCbURccjGpkrK4eM0IYHv3pM/ISQeoybp4oDa5KSY3C2lcbnEymJLs5tn7q23KVr+i+YoSSdRhbMolFk8J9yKicTZJy/ohTuSsVQdv4Uxcah2qVThGNEkPNkCJypWobw/6rVoV+Pl6IKZjta+leZONn02d1mg992QGScrRPQxkOlWPeb1ZEMzbnPBDBfLZD00vYVSChd06SMJs5V60z1bs4MFM/GoM1VcN21wqM/jfY+/gmMP3we/O2gPTBg3EjddfjYK8vPwzH//p/332649D5f+6YTQ8x977i2UlRTj2otPwyajR2DPXWbinFN+h0eeexNSKdVDc2jRBYQi7iCdqrbe/sDwZJc4J+TQLGmZa5qgh2yttKexJNbitulJfxRfyq16SIiIcYzfvAVbSNRIfYrRlHy+fKGYsNo66e1TAGz9W/3rr96Qvx7zDZopcT3hjY6o0hQtc2QjjmnR9YBaV0VXdjs5O9ns9GWyyjpeNXHGW/WY1pPJmdYCFo8DijU+BXv84sKh87N70nK9KZzu1FzrRIj51kSzxdrdlHMeX333M1SWl+Kis2ahakg5flmwFLPOvkrr40jUDK9C0HRhqK1bj2PPvhJXX3gq/vf87Zrj+MBTr2nV1lIJOY+G+yRCak70eaQWQWM3008837wd92m2jCik/o7Ekp9S/lUprXpCDcKbnBeP5KqRgFy/BljyM2whnvO42Q76MUfpETL7SiYj9ilVgZz8/gTY1nvq26d+le6OSl+PyVWjfWakisRl9GT9caXECUDRRDfcDjmPtZkRBeb1LLPpGE1HPGaL85glYwAV0WKNzmdpDj1gnOGz9sw7eak5j8gq59HR8YQPP/uG9i8WR556WZ+fUUj7oBMugm1Qi54CoyqtzRCPRhiR8ucccx1/+QJoa0xiFrAk8UiznKlNDF0Qls91dlayDWHrcBg0CcFPeaPb7K1//TW5jjY7WeZtRHmeex8fdvQcmhASuvkQTmii+d0k5rbZR//6y9dt2T7azQdd9Ml1pH2WUDwqYedR5vjIaKL3RachHusMYUKCQHL/zYT4esKPqzI7yi22eMys86jQzX5Pl96MW2yrTK6HRiP+926ghXp3MIx88diURc5jR7caMVJx8M22NpLjtbtoOhERTjUJp3m5k2bqX3+VOBQvPedxvJF7Shel/lyfhOvJDvEY4WTRmhKJMgoZ034nZ+mXL9N+75TyDPc7WQ+Lrl1mCDNniBy/l5NYPJIrS+P5mutsyb/U1iNu0ujmrT/BT6MBaZtRTrCN4Uk6hiJksghbkzv9yp325MUmWg9N0aFoBLmdZvGfKczFFyT8Kf80gyj0/neem13zo3+173zCDE7qzM5jFtQ/za9V0dmj4qeV1k2FjUM8GqMIzRcG0cDY9tnWm++iPy79qd+JGVJyDM1QQ2piibWQZNphawqdijFnMnIezRdXEiOJxKOobKfiJDsvytHO4+Y7A5tuqf/89fukF6EkQqGLPe0zClsnKpohV3a7/cPOKP2eXZBbpInHnORC1jTSzM5tFn0sCPFI2CSiE6GQlH3/KWQL1PQ+BI1QtSv3NBXEDT/DbKQ0aDmPKrrofi2Y+TB70wZgm0v96Ezjni27s0yt5jsSIgRiq/OohMfi/fRp/8+W6TzS3yUuyBbyHaWIWQrfklAhMdCRfhWpQheyZNZUMwEYNlbfxz98AFsxi0dyzn57nP79py/rbpbTJOMWk7ilQiaq7P35M3vXI27S+vuc0YQVu/MdCfMNB4mSRO7sYMTsXmd6NCHDDBKWGXJkVeYzM0JQa0fz7OvB6TzGEo/GRUM0w7WF0ZP09+7uBBbNTmEWsIScR7oYk4CgHB2LM4LTDlsL949Co7IcDNpvtB4hRkgokggy79tpO+uP87+1P+HfHLaeviuQV6Dnz30tuVtAslDFfk4/+0zc0JBwlDnnO1GYuL/PGX1WiFWxW3rZIh47Ta4jE0M8ZrhYhmEGCUu6FRy5wIXlqWeXZS0bmXg0TfRwImxNIUxi/jdJFU1Y6tMXD6MxNlbMs/wSrnTE49ipwHaGePwg9qQgS9B+yyvUxQilIxx3ud5O5O4LdWFJ2070V7TbVSPMvQy3/I3+9Xfv2hsKTriefo5rckdFBf7cz+1fT8jhTyAeaT00S5nWTv0m7cT8ORSV1kzsnMdOFo8M4xSvNGU+XC2TjSRsHdnjsU8POjsgMTFpm5RETNh5zJEnHlf+avklQn0nqVqW8uiShQTKgafrX89+X24bGPN+owkuFBYn4SGEG/W1zC/Uq9pXWhfOyRIS/DQZharbKU/s16+QMfqrkCdhTfuSqledCKv3d5NG3QAOPlP/+sePbS8aiSi6Muc7MjoROY8ctmYYZlCLx1g5jyIXy6aw9cSZeg89CtmuXpii05emeKT3HTo2becxokAlFZFN1cbU35EqSd9/2vL7x1yTeb9Rha5g+wP0NYqQ7NwvnEn2j24fNPezjObR9XtTFNo+DriO/eU8UiHNEefqTjJ9RiQfK/07jyweE4atM90gnGGYAcvAF4+xejwSEdMvFPtC1ilcpENOX7puKPXLc7n0eb3pNLKlQhcRfk02lD56it6aiC5Cr94tX0iZw7KVNeGfk/u4w4Hh9kROiaPodITZNhfopOM8kstHOaK0X21sX5RU2JrcbHIc6QagrQl46Xbb8y81zDdEoscjEztszTmPDMMMWvEYq8cjYe4bJjvvkaqMaaJMinl30qqtxxjvvcJ6yFpbD/1PqqF0Me6OQpB2TOowO1lDhutfi7D4zofpIdm1Sy0XCaUlRmh7N5lmJGeCRHmqwnVcNtexYpHwVKDcKOF4BrDpVvrNwEu3OZd/KMaSEuw8Ju4VyuKRYZhBKx5j9Hjs4xjJbtczcqJ+gaS8shSaDkuZ6CLcvzRD1iFCI++SWBOFqidupX/9vT7LXDpCjOTkAxWGeKRwJ7lXAqdcR209/uxxHc3iqM8NiAJM3dG5QqJ4LbEoR/XQs/XxjSRUXrlLb6buFGaxzwUziXMeOWzNMMygFY+x8h2N5rwxXRFZs6yJ2iUp/ZoU55Hyx4aOktczLxXnccs9dHFADpxdxRjCeayq0UU2iRMKz1Oza4IEiYMFK8qGZj0M3NoILPweGSee80jHJN1Ikfu+6Afn1hORW6wAh/4RmLytfly99J+kWljJhAtm+oH7PDIMI4GB36qnJLZ4DPWgI6EmO2w9XIjH1NqOSKm2psbg5HqSeJPhrCTrhlK4eMbu+tffvw/b+wZSE3CicZ0+l3nOR0D1KKBhtaOOiUI9Jh+7Vg8DOzhNJu56/D59+0Qf01SFLhrGO1nQ4zc5j+M31/Nh6f1fvA1Y+rNz6with/s8JoRzHhmGkYBno+zx2EesSXQeqfhm+Dj9a8q9c3qWtIT+jjHFWn9ha2pLRLmeFD62000SYdChY/THRsPhpG331kPICCnuZ1uJV20tejtaHFVplfAY0FxgmjGq84cPMyMciQjnkcPWfeCcR4ZhJDDwxWPZkD49HgUufy8Csns9Vo7QK7wpPJhi0YaU2dYy8x2JQJJTb0ShzJwP7XXgxDaibUxQOyAmsVNMlejk1FLlPM1YdxIh9ikfVkyRcTLnMl6OKolInpkcXzwGachuR6ZXwzDMAGXjyXk0t+mx03kcMV5/XLcs5T6DaU10IaglEYVuCVkNskMFMzlhIUJ9JM0MqdGbZNOFh8LHdmKukidYPEYSK29WuI5UmOJ0EYTYXzRxiNZEaQV2VOEniULnATqmaYQkE9+ZpXxHJ/qkMgyzUTKwnUcShQUl+tdU0GDnRJc08x2lOI/jjby2dSvkhZzMThYJxzP+qTuqj1wVfs7ErfVHCkXaHApURA6dwKmWPAMFX4wbECEeZU76SVWMUN/RTLuOdPyQeL7nIqCnM6PryFYUuhn77j29UwTDMMygE49U9fubo0w9HvteLMLV1jnyK60t5MGl3aqHihGIhd9BGuY1kbuYk6vndNLfKQSyEI9OVBubnUdyOmmCDxN/PjoVMpHrRyz50fn1iLC1CIX+8gUyTruprRPTpwsF3ns808tgGGaAMzDFIzlkh/2f3m+R+PTlmE+jnMdQMr8MSGCJsLEV5zGUX2hBzFIO4Lhp+tcL5InHUMEM/W2iZyZBffrobyyu0MUk5dMt/sHZalkSjllQ4ZxVRN8Q0bQhSjOgqnBypJ3GLPaXz9XXwTAMw2zUDLycRxJRJ1ylC0dK+H7+FuD792I+VXrYmiqAyfGkC6QFdyM03YFeg9rtpAKFJkngNa6V22PRHEqvHh3++ZTt9MryTbfUv1+92Jl8OrOTxfmO/Td1D1VZk+uoZlY8ZjhkzTAMwzjDwHMeqc9haaU+0/nJGxKGNaWHrUWxjAXXUV9P1NSb3u7kf3niTOmuo4a5iMcsHsndpTGIToaso5s8c75jX6JTH0R/x8XOh6wJRfRS7O4EFjrbEJxhGIbJDANPPIqCFZrf208+nCvdamtyCA88TZ9U8dkr4fdem9pkmT5ha3HxT1Y8kss0YQv5+Y7mAgwSiyTKiV+/1MPWW+2pi3XCqUkhZieLnce+hI5pr96ep2KYnhtKIeMMoLTUA6/eDbQ0ONucnGEYhskYA1A8Jt+gO+RiWa1urpkQnhdMYVy3Oz3nUYgjch1TWdO4qXq4vq1R/pxgIWhFIRCJAJogQ+JRFOhQ+xWnClfYeUy+Yv+3s/Sv53+Tmostm1++zNx7MwzDMI4zAMWjcP/6F1Fp5zxWjQx/TU2QBdTjMR1xlKp4nGRTyFqshxAh6/pVwOpFeusj4UQ6GY4UziMV6FB+JxNH7I/XK60pR/TD5zK9KoZhGGYQ4RpwDcGpUTaF6epXpjAOME3x+O07etia3nfVQj2/yyqpNgqn0PmmW9kmHkMCm4QI0bBKL7yg0LXAqZA1rYdmlJOgJXeXw6DxxbXYX1+8xq1pGIZhGEfxDEjXkdwx84zWOIRa9Vh1HmmyinA5qX/dd+8CvVFNrK3mGCa7Jqoqzy8CqDBh9UJIx1zEQ4jJHHM/B7bbDyAxJztUngCFJl/cfSGPlouHOW+WUgy+eSuTq2EYhmEGIQNTPCbZoDtcbZ2bnvNIOX+EhKkuEX0Vk0G0yqHpIXaME4sWj8LRpXZAj/7NaM/jcAsY7hWY3P56/+m++49hGIZhbMYzMItlltk/DpCqj8nxC0rOvUt1TUI8LrJp9JxZfJCr2lwf/p5HmGUdCon6NYt1cS+78p5hGIZhNirxSA2rqTWJJecxx7rr2LwuMlSYLqlUgFeOAMqH6gJv2c/y1hBrPeZ8RyZr0RrNP3ZNppfBMAzDDGIGTsFMxXB9DBu5Y0n2/wv3ebQgHodEhaxlkcp8a+E6rvg1cvKKRCJ6TyZRhMQwDMMwzOBm4IhH0YewbrnexsXunMdQvqPEUYCphq1DIWsbZ0qbw9ZUiMQwDMMwDLNRiMcU8x3TbhJeVWOL8xgWtP04j9SSiJqUE4vtFI+9fSutGYZhGIZhBr54TK3SOiJsnbJ4VGwMWycpaMdvASguvWiF5njbhd8flfPIMAzDMAwz0AtmqFG2mICSgni0PGGmbAiQk6sLPdlj+ZIVtKIxuJ0h61BTbp+e75jJEXcMwzAMwwwIslM8lldHtowZNVEvMKE+i+afJx229uouXpK5kiHXcf3a5H/HasHM0DF6A3Czu0jCctw0+0PWoin3PdyUm2EYhmGYgRy2PvPmyLnSM3bXH+d9k9LLhJzHVN1H8d7rJYesCbOgpQryk64GTrk+8u894BQgJ0+fIOJEr0USruw6MgzDMAwzYMUjMXNv/TG/GJi0jf71nA+tt6FJJe8xerKMRCIal0/ZVg/JUzPyoy8GyqqBnQ4BNttBH7/4xv3S359hGIZhGGbjFI9Td9RF1eY7A26PnutYtyKll1Dof8Qs6pScR3sqrTXMjcsnbq1/TSFjmmhz/F+BXY/Qf/bOY8DK+fLfn2EYhmEYxmnxeNJR++PrNx/A0q9fxOuP34wZ0zZN6vcO2WcX1M55DQ/9+/LET6R2PCSuZvwG2PI3+s9+SM11DJFqr0dyAmmyi13i0Wesp3K4PjGHxh/SxJCmdbqAJL55G/jxI/nvzTAMwzAM47R4PHjvnXHVBafilnufxj7HnItfFy7DU3ddg8ry0oS/N3JENa44/2R89f3c/t/k+/f0x50OBiqG6c7cr1/BEr4UnUcSdeR00nu2NUI6FI42tx5avVCfU/zMP/SZxT9+DHzwjPz3ZRiGYRiGyYR4PP34Q/HUS+/g2f++j0VLV+Ev192Fru4eHHPoXvHfxOXCnTdcgH/d/RRWrEmi9Q0JxY7WsFv4yxfWx/Ol2ih89GT9sXaJtfdLdj2Chd/rj9QyhxzINx+UX+HNMAzDMAyTiVY9Xo8H06dMwB0PvRD6maqq+PTrOdh6+qS4v3f+GUdjfVMrnn7lPWy71dR+38cdDECd8xGCVDxCi/zpE7jc7lSWCq/xfGoUTlLMk5uX1Gv4xmwGldawaj7cKb5nMuvxBP0ImH++ZA4Uie+T6nrEY6bh9SSG15MYXk9ieD2J4fUkhtczuNbTGzCrFAnisaK8BB6PGw2NkRNP1je2YMJYU6sZE9vO2AxHH7oX9j7qz0m/T3FeHkqWfoc1W+yK3PWrMbS3DSgz8gFTxKsGQJ5lRWk5Cvt5DRUKVo6ZoonH6qbVyLP4noko83ogvNec9WtQo/gt/20yqC4uRjbB60kMrycxvJ7E8HoSw+tJDK9ncKxnWWNjZpuEFxbk4z/Xn4+LrrkDTS1tSf9ee3c3Oteuhvuu80EZgmssvDcpbtpwvm69+XVjrx8tLS0JfydYPRrBvEIt33H9op+hSAwfi/W0trWGfhaY/y3W9LMmuxDrqW9vhy+JuwxeD6+H18Pr4fXweng9vJ6UxWNTcxv8/gCqKssjfj6ksgwN6/vOXx47ahhG1wzFo7ddEfqZy6U10MHK717BLoeeiRWr1/X5vYCqIiDpgFCNXMmA29P/a440Qu+rFsBnbjAuEb9oHURrWvCttL/TKvTBS8aidgpeT2J4PYnh9SSG15MYXk9ieD2J8Q2i9aQkHn1+P36atxg7bzsdb3+oVz8rioKdt90CjzzzRp/nL162Gr854o8RP/vL/x2vOZJX/uM+1K5bD7tRfL1aGDqpVj1jN9MfV/xq33paG/Tek421QP0q296HYRiGYRjGDlIOW9/3+Cu49drz8OOvi/HD3IU4bdYhKMjPwzP//Z/232+79jysq2/Ejbc/hp5eHxYsWRnx+63tHdpj9M9tI161dW4BcPRFQONa4PX79NnXowznccU825aj0HxumiVtk7PJMAzDMAyTVeLx1Xc/03o6XnTWLFQNKccvC5Zi1tlXYX2TnrtXM7wKQVXz+rKDeH0eqfn4iPH6P2rLs24ZkJsPkLirs1nYUhsihmEYhmGYAYilgpmHn31D+xeLI0+9LOHvnnflrXAUMdHFHLamKTJb/zb8/R5HA/O+0b/WRgJmkfhlGIZhGIbJIrJ3trUklFDY2hv+4aSZQEml7gAu+0UXltN3sT3fkWEYhmEYZqCz0YvHmLOtt9lHf5z9vp7vSKFqgY35jgzDMAzDMAOdjV88+qIKZmom6P+oYGX2B8CGZuCdR/X/1tqoz5lmGIZhGIZhnG8SnhWEch4N8TjTcB1/+RLoNBqXz/sa6O4A2poytEiGYRiGYZiBgWfwhK1zgKFjgMkz9e+/eyfyecvmOr82hmEYhmGYAcZGH7ZWRKseKpA58ly90nrBd9ygm2EYhmEYxgKDJ2xdPlR/XF8LvHF/RpfEMAzDMAwzUHENmrA1QVXVL/wb6OnK5IoYhmEYhmEGLBu9eNTGARLBAPDyHUBzXaaXxDAMwzAMM2DZ6MPWConFtx8Gmuu5ATjDMAzDMEyabPTiUeOHDzO9AoZhGIZhmI2CjT5szTAMwzAMw8iDxSPDMAzDMAyTNCweGYZhGIZhmKRh8cgwDMMwDMMkDYtHhmEYhmEYJmlYPDIMwzAMwzBJw+KRYRiGYRiGSRoWjwzDMAzDMEzSsHhkGIZhGIZhkkZB9dZq8k9nGIZhGIZhBjPsPDIMwzAMwzBJw+KRYRiGYRiGSRoWjwzDMAzDMEzSsHhkGIZhGIZhkobFI8MwDMMwDJM0nuSfymy31VScfeLh2HzKeAyrrsTJ512Ptz/8KvTfh1SU4fJzT8Ju289AaXERvpo9F3/9+71YtnJt6DljRg7DleefjG1nbIacHC8+/GI2/nrTvVjf1BJ6ziO3/hVTJ22CyopStLZtwKdf/4jrb3sEdQ1NyGac2j5fv/kARo0YGvHeN9z2KO54+AUM9u2zw8xpePGBG2O+/36zzsePvyzCYD9+Np88HpefeyK2mLopAoEg3nz/C1x984Po7OpGtvJ/Jx+J/ffcERPG1qC7pxff/Tgf19/6CJasWBN6Tm6OF1ddcAoO3mcX7euPvvgBl95wd8TfXjOsCjdefhZ2mjkdHV1deP61D3DDfx7VtgNRPaRce43pm03AuFHD8eDTr+Gqfz6AbMep7UPHFR0748eORH5eLtasbcDjL76N+5/4L7IZp7ZPvPPPFnsej4bG8OsM1u3z72vOxVEH79nn/RcsWYnfHPFHDCTYeUyBgvw8/LJwGS678Z6Y//2hf1+OMTVD8YfzrsfeR/8Zq9c24Nl7rtNOMgQ9Pn33NVBVFb87/XIcctLFyPF68Oh/roCiKKHX+fy7n3HGxX/HLoeeidMuvBFjRw3D/TdfgmzHqe1D/OPOJ7QTkvhHF7lsx4nt892c+RHbhf49+dI7WLF6XVYLR6e2z9CqCjxz77Wa4DzwuAsx649XY9L40bj1mnORzeyw9TQ88uwbOPCEi3D0mVfA43Frf6v424mrLzwVe+26Lc646O84/JRLtb/1wVsuDf13l8uFx26/UtsmB590Ef58xa34/UF74qKzZ4WeQ4K7sbkVt93/LH5duAwDBae2D91gPPzMG9rv73b42bj1/mfxlz8eh1lH7INsxqntI9j54DMizkHrm1qRzTi1fa78x30R22XrvU9CU0sbXn/vMww0uM+jRWrnvBbhjGwyegQ+e/Ve7H7EH7FwyUrtZ3TB+vH9x3DT7Y/jqZffxW47bIkn7rgKU3Y9Bhs6urTnFBcVYN4nT+OYs67UHMZY7L3bttqFc+y2h8PvD2Cwbx9yHu9/8lU88OSrGKg4dfzQSXD2u4/goadf1y50g3370EX+4rNnYcZvT9REJjF5whh88MId2PGg07F8VdjFzGYqyksw98MncdjJl+Dr2b9of+fPHz6BP156M9743xfacyaMHYlPXrkbBx5/IWb/vAC/2WlrPPafK7DlXieF3JLjj9wXl//5JGz+m+Pg8/sj3uOFB27ALwuWDgjnMRPbR/DAvy5FZ1cPzvnrLRjs20c4j5N3ORpt7R0YqDh1/Oz7m+2142e7A07VXOyBBDuPkqA7dqKnpzf0M7o49fb6sM2Wm+nP8XpA1yv6mYCeHwyq2NZ4TjRlJUU4fP/dNRt9oAhHJ7bP//3hSMz96Em8+8ytOOvEw+B2D+xD2a7jZ+/dtkN5aTGe/e//MJCRtX1yvV74fP6QcCQoTEXE24bZSElRofbY0tquPU6fMgE5Xm/EDcTi5auxurYeW28xWft+5vTJmL94RUSYjUJvJcWFmvu6MeHU9pk2aRPM3GIKvvp+LgYSdm+f9569DT+89yieuecabDNjCgYaTh0/xxy6l/aaA004EgP7iptFiAPp0nNORGlxIbweD/540hEYMawKQ4eUa8/5/ucFWtiD8rbIDqd/lJ9F7lD1kIqI17v8zydi8ZfP49dPntZe4w/nXoeBjMzt8+BTr+GsS/6B3512OR5/4W386ZTf46/n/gEDGdnHj+CYw/bCR1/+gLX1jRjIyNo+n337E6oqy7UbDnoNeq3LzjkxlO83ECDH9W8XnYZvfvhVy5USa+/p9fVxexqaWlBdWaZ9XTWkrE/embjQVQ2Qvz1bts937zyMZd+8hLeeukULd5LzPVCwc/vUNzTj4mvvxKkX3KilXNWuW48X7r9ByzMeKDj1+RpaVaG5lQPp2DHD4lES5AqecsENGD9mBOZ9+gyWfPUCdtxmc7z/2Xea80E0NbdpuYyUN7Hoi+ew4LNnUVJchJ9+XYxgUE+oFdz96MvY+6g/a/kX9N9uu+48DGRkbp/7nvgvvvxuLuYtWq6Jx2v+9SBOPvpAzXkaqMg+fojh1ZXYfYct8fTL72GgI2v7UMj73CtvxRnHH6a9xpz3H8eq2jrUr2+GarxOtnPDpWdi8oTROOsv/8j0Ugbt9jnsD5dgv2PPw1+uvwunzjoYh+67KwYKdm4fKjB54sW38fO8JVq07Pyr/6M9nnbcIRgoOPX5+t1Be2hi9O0PwkWBA4mBe7XNQugDs9dRf9byI7xej3Yxe/3xm7WLl+DjL3/QcqsqykrgDwS0g2fO/x7DyjXrIl6Lkmjp39KVtVi0dBW+f/cRbD19Er7/aQEGKjK3j5nZcxdqr0cV2ObquMG+fY465Ldobm3Hux9/jY0BWdvn5bc+1v5R9TY5lRTCPv24Q7AiwTGWLVx/yRnYa9dtcNjJl0a4ySR+qQKUQmRmd6Sqogz1hhvSsL4FW06bGPF6tA30/9aMjQGntg/dcBAUpqTXuODMY/DK258g28nE8TPnl4XYZsbASAlxcvscfeheeOGND+Pm0mY77DzaQPuGTu3CNm70cGyx2QS881HfizcJQzoId9pmOoZUlOLdj76J+3pUxWXO+xroyN4+UyeNQyAQiMg1GcjI2j4kHl947cMBnStr5/ah44XE4yH77KKFpD75ag6y/cK27x47aJXkQrwIfpq3GL0+H3bedovQz8aPqcHIEdX4/sf52vff/TRfKw6qLC8NPWfXHWZo22nhUj08N5DJ1Pah8/NAODdnavvQ+bl+fXa3mXN6++wwc5pWBPj0AA1ZE+w8pthKhC5YglE1Q7UPRkvrBqxZ14AD99pJa3NBya9TNh2Lay4+DW9/+LXmhgiOOmRPLFq6Wnve1tMna8+hMKxwzOjOZcbUTfHNnF/R0rYBY0cOx8V/nIVlK2tDB+lg3j7kvm65+SR88e1PWkUtJSv/7cJT8eKbH6E1y6v7nNg+gp23na71PBxI+TRObZ8/HHWAFkrr6OzSTu5XnHuy1ostm6tDb7jsLBy23674w7nXa8d9lZFnRUKaCn7okdITrr7gFC3Jv72jU7sYfvfjPK0SlKDttHDpKtx+/fm47taHtdxPajPzyHNvoNcXdj9omxOF+XnahZC+p/9OEZDBvn1OOmp/7fijHFxi+62m4cwTDsv6VmFObR8K4a9aU6flCpJTd+zhe2s3cNTtIJtx8vNFHHPo3vj+p/mhnMqBCLfqSYF4DVCfffV9nHflrTjlmIO0RPwhlWVa4vDzr3+AW+97NsKWpuT83x+8J8pKi7Cqth6PP/+WdnET0J0LXfA2mzhOu5iSXf7h59/jtgeexbr67L57c2L7UOL1DZediQnjRmrVb3SiIuv/vsdf6fMBHYzbR3DnjRdi5PAqHHLSXzBQcGr73Hbtedhzl5koLMjH4mWrcc9jL+PFNz5EtrcuigXlbz736vsRTYwP2XdXo4nxbK2JsTmJv2Z4FW66/GzsuPXmmutKTYyv/88joSbG8d6LnJjt9j8Vg337UG71cUfui9E1QzVHn/qnUh9Vyr02V/AP1u1z9kmHY9bh+2hN/ru6e7S89H/f+wy++O5nZDNOfr6Kiwow573HcMU/78NTLw2cm/toWDwyDMMwDMMwScM5jwzDMAzDMEzSsHhkGIZhGIZhkobFI8MwDMMwDJM0LB4ZhmEYhmGYpGHxyDAMwzAMwyQNi0eGYRiGYRgmaVg8MgzDMAzDMEnD4pFhmI0WmjkcrwFwpsjGNTEMw6QCi0eGYZgoTvz9/tqkGqvk5+VqIpGm5jAMw2xssHhkGIaxRTweix1nbt7nv916/7MYt+3haa6QYRgmc3gy+N4MwzCDDppza551yzAMM9Dg2dYMw2wUbDtjM1x90amYPGEM1tU34q5HXsLQqnLNARwx4yDtOUcdsieOOOA32nOKiwqxYtVaPPTM63js+bdCr/P1mw9g1IihEa/9xXc/48hTL9O+Liku1ELSB+y5IyorylC7rgFPvfQu7nr0JaiqipEjqvHNmw/2Wd+/7nkK/7rnae13zWsiKAfy4Wdex5ffz8WFZx6LUTVD8cuCZbj42jswf/EKHHfEvjjrxMMwfOgQzP55Ac698lasrq2PeP0tp03EhWcdi62nT4bX48GcXxbhpjsew7dz5knf1gzDDG7YeWQYZsBDYvDpu69BY3MrbrnnabjdLk1INTS2RDzvhN/tj4VLVuLdj79BwB/AXrtti5suPxsul4JHnn1Te85V/3wA1/3ldHR0duO2B57Tfra+qSUUjn7xgRsxvLoSj7/4NtasbcDMGZNx6TknoLqqXPvdxqZW/OW6O/H3v/4Rb77/Bd58/0vtd+ctWp7wb9h2y6nYe7ft8Mizb2jf/98pR+Kx/1yJux59ESf+/gA8+tybKC0pwtknHYFbrj4Hvz/9r6Hf3Wmb6Xjizqvx87zFuOXepxFUVRx18G/x3H3X47CT/4I5cxdJ3uIMwwxmWDwyDDPguejsWRRHwWEnX4I16xq0n73x/hf44Pk7Ip53xCmXorunN/T9w8++gSfvvBqnH3doSDy+/eFXuPiPx6GppQ0vvflRxO+fftwhGDtqGPY++s9YtnKt9rMnXnwbdfVNOOvEw3HvY6+gtm493vjfF5p4JMEY/RrxGD+2BrsedlbIUWxp34B/XvF/+POpR2HnQ85ER2eX9nMSxuec8nvN4RTPvemvZ+OLb3/CrD9eHXq9J154Gx++eCf+8sfjccxZV1rargzDMLHgghmGYQY0LpcLu++wFd758KuQcCQWL1uNj76cHfFcs3AsLipARVmJFioeO2q49n1/HLjXzvh69q9obevQflf8+/TrOfB43Nhu66mW/47PvvkxIhT9w88LtEdyL4Vw1H++UHscUzNMe5w2aROMH1ODl9/6OGJNBfl52mtut9VUKIpieV0MwzDRsPPIMMyAprK8BPn5uSEn0MyS5Wvw2122CX2/zYwpWk7h1ltM1sSVmZKiQrRv6Ez4XpuMHoGpk8Zh7kdPxvzvQyrKLP8dFAI302aspXbd+qifd2iPFMImxo0ZoT3+57rz4752SVEBWtv132MYhkkXFo8MwwwKxowchmfvvQ5Llq/G1Tc/iNq6Bvh8fuyx80yccfyhUFz9u3P0nI+//AF3PfJizP++dEWt5fUFg7ErsANxfi7MRJfxxTW3PIRfFiyN+dyOrm7L62IYhomGxSPDMAOaxuY2dHX1YNzo4THzCAVUHJOXm4OT/nxdRHh7x22m9/k9qpqOxYrV61BYkIdPv/4x4Zri/b4dLF+9Tnts7+jsd10MwzAy4JxHhmEGNOTYUW7jPr/ZHjXDqkI/nzBupJYLGXqe6K1oMhgpz/GoGM3AO7u6UVpc2Ofnr737KWZuMQW77bBln/9GLXyomIXo6u4J/cxufvp1MZatrMWZJxzWJxRPVJSX2L4GhmEGF+w8Mgwz4Ln57qew+45b4eWHbtJa2rg9bpx89IFYsGSllqNIULi5p9eHR2+7QquQLszPx7GH76219xlWXRnxej/PW4ITfrcf/nzq77F81Vqsb2rF59/+hLsffVlrp0MtdJ577X1NuJFgm7zpWBz42x2x3f6nalXaVJhD733w3rtooeyW1natXyP9TDbkcl54ze144o6r8dGLd+LZV/+HtfWNWjuhHWdOx4aOTpz452ulvy/DMIMXFo8Mwwx4qCXOsWdfhasvOAUXnj0La+vWa4KSmoQL8bhkxRqcfuGNuPiPx+OK807WekA+9vybmnj899/OjXi9W+59BjXDq7WeiuROUpNwEo/kKB5+yqU459TfaZXXRx64BzZs6MTSlWu09xPFLMSFf/sPrvvLGbj6wlORm+PVmoTbIR6JL7+bi4NPvAjnnnY0/nDUgSgoyENDY7NWmf34C2/b8p4MwwxeeMIMwzAMwzAMkzSc88gwDMMwDMMkDYtHhmEYhmEYJmlYPDIMwzAMwzBJw+KRYRiGYRiGSRoWjwzDMAzDMEzSsHhkGIZhGIZhkobFI8MwDMMwDJM0LB4ZhmEYhmGYpGHxyDAMwzAMwyQNi0eGYRiGYRgmaVg8MgzDMAzDMEnD4pFhGIZhGIZJGhaPDMMwDMMwDJLl/wHZomBEhxcFRgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data.loc[:end_train].plot(ax=ax, label='train')\n",
    "data.loc[end_train:].plot(ax=ax, label='test')\n",
    "predictions_backtest['pred'].plot(ax=ax, label='predictions backtest')\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6d257fbe-f14e-4b5c-b21c-82b1fa87d511",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning and lags selection\n",
    "\n",
    "Hyperparameter tuning is a crucial aspect of developing accurate and effective machine learning models. In machine learning, hyperparameters are values that cannot be learned from data and must be set by the user before the model is trained. These hyperparameters can significantly impact the performance of the model, and tuning them carefully can improve its accuracy and generalization to new data. In the case of forecasting models, the lags included in the model can be considered as an additional hyperparameter.\n",
    "\n",
    "Hyperparameter tuning involves systematically testing different values or combinations of hyperparameters (including lags) to find the optimal configuration that produces the best results. The Skforecast library offers various hyperparameter tuning strategies, including grid search, random search, and Bayesian search. For more detailed documentation on Hyperparameter tuning, visit: [Hyperparameter tuning and lags selection](../user_guides/hyperparameter-tuning-and-lags-selection.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bef7b90a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a2adb138cdf49a193289c8da30022ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "lags grid:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "996fa9cd91614d6b9be8a24cc42426fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "params grid:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10] \n",
      "  Parameters: {'max_depth': 15, 'n_estimators': 50}\n",
      "  Backtesting metric: 0.017861588026122758\n"
     ]
    }
   ],
   "source": [
    "# Grid search hyperparameter and lags\n",
    "# ==============================================================================\n",
    "# Estimator hyperparameters\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100],\n",
    "    'max_depth': [5, 10, 15]\n",
    "}\n",
    "\n",
    "# Lags used as predictors\n",
    "lags_grid = [3, 10, [1, 2, 3, 20]]\n",
    "\n",
    "# Folds\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 10,\n",
    "         initial_train_size = len(data.loc[:end_train]),\n",
    "         refit              = False,\n",
    "     )\n",
    "\n",
    "results_grid = grid_search_forecaster(\n",
    "                   forecaster  = forecaster,\n",
    "                   y           = data,\n",
    "                   param_grid  = param_grid,\n",
    "                   lags_grid   = lags_grid,\n",
    "                   cv          = cv,\n",
    "                   metric      = 'mean_squared_error',\n",
    "                   return_best = True,\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b947b888",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>lags</th>\n",
       "      <th>lags_label</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_squared_error</th>\n",
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       "      <td>[1, 2, 3]</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 50}</td>\n",
       "      <td>0.035032</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 50}</td>\n",
       "      <td>0.035168</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>0.040312</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
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       "      <td>[1, 2, 3]</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 100}</td>\n",
       "      <td>0.040312</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>0.040562</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>0.042146</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>0.042147</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 100}</td>\n",
       "      <td>0.042147</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 50}</td>\n",
       "      <td>0.043385</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 50}</td>\n",
       "      <td>0.043385</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>[1, 2, 3, 20]</td>\n",
       "      <td>{'max_depth': 15, 'n_estimators': 50}</td>\n",
       "      <td>0.043385</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                               lags                       lags_label  \\\n",
       "0   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "1   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "2   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "3   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "4   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "5   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "6                         [1, 2, 3]                        [1, 2, 3]   \n",
       "7                         [1, 2, 3]                        [1, 2, 3]   \n",
       "8                         [1, 2, 3]                        [1, 2, 3]   \n",
       "9                         [1, 2, 3]                        [1, 2, 3]   \n",
       "10                        [1, 2, 3]                        [1, 2, 3]   \n",
       "11                        [1, 2, 3]                        [1, 2, 3]   \n",
       "12                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "13                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "14                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "15                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "16                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "17                    [1, 2, 3, 20]                    [1, 2, 3, 20]   \n",
       "\n",
       "                                    params  mean_squared_error  max_depth  \\\n",
       "0    {'max_depth': 15, 'n_estimators': 50}            0.017862         15   \n",
       "1    {'max_depth': 10, 'n_estimators': 50}            0.017862         10   \n",
       "2    {'max_depth': 5, 'n_estimators': 100}            0.018772          5   \n",
       "3   {'max_depth': 10, 'n_estimators': 100}            0.018898         10   \n",
       "4   {'max_depth': 15, 'n_estimators': 100}            0.018898         15   \n",
       "5     {'max_depth': 5, 'n_estimators': 50}            0.019198          5   \n",
       "6    {'max_depth': 10, 'n_estimators': 50}            0.035032         10   \n",
       "7    {'max_depth': 15, 'n_estimators': 50}            0.035032         15   \n",
       "8     {'max_depth': 5, 'n_estimators': 50}            0.035168          5   \n",
       "9   {'max_depth': 10, 'n_estimators': 100}            0.040312         10   \n",
       "10  {'max_depth': 15, 'n_estimators': 100}            0.040312         15   \n",
       "11   {'max_depth': 5, 'n_estimators': 100}            0.040562          5   \n",
       "12   {'max_depth': 5, 'n_estimators': 100}            0.042146          5   \n",
       "13  {'max_depth': 10, 'n_estimators': 100}            0.042147         10   \n",
       "14  {'max_depth': 15, 'n_estimators': 100}            0.042147         15   \n",
       "15    {'max_depth': 5, 'n_estimators': 50}            0.043385          5   \n",
       "16   {'max_depth': 10, 'n_estimators': 50}            0.043385         10   \n",
       "17   {'max_depth': 15, 'n_estimators': 50}            0.043385         15   \n",
       "\n",
       "    n_estimators  \n",
       "0             50  \n",
       "1             50  \n",
       "2            100  \n",
       "3            100  \n",
       "4            100  \n",
       "5             50  \n",
       "6             50  \n",
       "7             50  \n",
       "8             50  \n",
       "9            100  \n",
       "10           100  \n",
       "11           100  \n",
       "12           100  \n",
       "13           100  \n",
       "14           100  \n",
       "15            50  \n",
       "16            50  \n",
       "17            50  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid results\n",
    "# ==============================================================================\n",
    "results_grid"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4edc0fdc",
   "metadata": {},
   "source": [
    "Since `return_best = True`, the forecaster object is updated with the best configuration found and trained with the whole data set. This means that the final model obtained from grid search will have the best combination of lags and hyperparameters that resulted in the highest performance metric. This final model can then be used for future predictions on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40d6a949",
   "metadata": {},
   "outputs": [
    {
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       "        <div class=\"container-ab4c94f2d79f485ab6dc3c83fec5dfe7\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_10']</li>\n",
       "                    <li><strong>Window size:</strong> 10</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 14:31:13</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 14:31:17</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1991-07-01 00:00:00'), Timestamp('2008-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 15, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 50, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1, 'device': 'cpu'}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10] \n",
       "Window features: ['roll_mean_10'] \n",
       "Window size: 10 \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2008-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 15,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 50, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1, 'device':\n",
       "    'cpu'} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 14:31:13 \n",
       "Last fit date: 2025-11-26 14:31:17 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print forecaster information\n",
    "# ==============================================================================\n",
    "forecaster"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "skforecast_py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
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 "nbformat_minor": 5
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